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# A Survey of Generative AI in Finance

Zhengliang Liu, Zhenyuan Yang, Ruidong Zhang, Huaqin Zhao, Jing Zhang, Yiheng Liu, Yiwei Li, Peng Shu, Hanqi Jiang, Junhao Chen, et al.

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## To cite this version:

Zhengliang Liu, Zhenyuan Yang, Ruidong Zhang, Huaqin Zhao, Jing Zhang, et al.. A Survey of Generative AI in Finance. 2025. hal-05020829

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Distributed under a Creative Commons Attribution 4.0 International License
# A Survey of Generative AI in Finance

Zhengliang Liu*¹, Zhenyuan Yang*¹, Ruidong Zhang¹, Huaqin Zhao¹, Jing Zhang¹, Yiheng Liu¹, Yiwei Li¹, Peng Shu¹, Hanqi Jiang¹, Junhao Chen¹, Zihao Wu¹, Yifan Zhou¹, Yi Pan¹, Shaochen Xu¹, Xiaowei Yu¹, Chao Cao¹, Enze Shi¹, Huawen Hu¹, Hao Chen¹, Tuo Zhang¹, Xintao Hu¹, Shu Zhang¹, Dajiang Zhu¹, and Tianming Liu¹

¹GyriFin Interest Group of Finance Foundation Models

*These authors contributed equally to this work.

## Abstract

This paper offers a comprehensive survey of generative AI applications in the financial sector through an analysis of real-world implementations across diverse regions and industry segments. Our research maps various AI solutions from major financial institutions such as BlackRock's Aladdin Copilot and Wells Fargo's Fargo Assistant. We classify these tools according to geographical distribution (North America, Europe, and Asia) and industry sectors (investment banking, wealth management, retail banking, and consulting services). The study delves into the technological approaches, core functionalities, and quantifiable impact of these AI tools, which reveals distinct regional patterns in adoption strategies. Our findings demonstrate that while generative AI adoption in finance continues to accelerate rapidly, implementation approaches differ substantially by region and industry segment due to local market conditions and regulatory environments. This analysis provides valuable insights for financial institutions that consider similar AI deployments and highlights the dynamic landscape of AI-enhanced financial services.

## Contents

1 **Introduction** 6

2 **Related Work** 6
<table><tr><td>3</td><td>Gen AI Tools</td><td>8</td></tr><tr><td>3.1</td><td>Citadel LangCheck</td><td>8</td></tr><tr><td>3.1.1</td><td>Product Overview</td><td>8</td></tr><tr><td>3.1.2</td><td>Technology</td><td>9</td></tr><tr><td>3.1.3</td><td>Functionalities</td><td>9</td></tr><tr><td>3.1.4</td><td>Impact</td><td>10</td></tr><tr><td>3.1.5</td><td>Our Review</td><td>10</td></tr><tr><td>3.2</td><td>Citadel Lens for LLMs</td><td>11</td></tr><tr><td>3.2.1</td><td>Product Overview</td><td>11</td></tr><tr><td>3.2.2</td><td>Technology</td><td>11</td></tr><tr><td>3.2.3</td><td>Functionalities</td><td>11</td></tr><tr><td>3.2.4</td><td>Performance &amp; Impact</td><td>12</td></tr><tr><td>3.2.5</td><td>Our Review</td><td>12</td></tr><tr><td>3.3</td><td>McKinsey Lilli</td><td>12</td></tr><tr><td>3.3.1</td><td>Product Overview</td><td>12</td></tr><tr><td>3.3.2</td><td>Technology</td><td>12</td></tr><tr><td>3.3.3</td><td>Functionalities</td><td>12</td></tr><tr><td>3.3.4</td><td>Performance &amp; Impact</td><td>13</td></tr><tr><td>3.3.5</td><td>Our Review</td><td>13</td></tr><tr><td>3.4</td><td>BloombergGPT</td><td>13</td></tr><tr><td>3.4.1</td><td>Product Overview</td><td>13</td></tr><tr><td>3.4.2</td><td>Technology</td><td>13</td></tr><tr><td>3.4.3</td><td>Functionalities</td><td>14</td></tr><tr><td>3.4.4</td><td>Performance &amp; Impact</td><td>14</td></tr><tr><td>3.4.5</td><td>Our Review</td><td>14</td></tr><tr><td>3.5</td><td>TIFIN Sage</td><td>14</td></tr><tr><td>3.5.1</td><td>Product Overview</td><td>14</td></tr><tr><td>3.5.2</td><td>Technology</td><td>15</td></tr><tr><td>3.5.3</td><td>Functionalities</td><td>15</td></tr><tr><td>3.5.4</td><td>Performance &amp; Impact</td><td>15</td></tr><tr><td>3.5.5</td><td>Our Review</td><td>15</td></tr><tr><td>3.6</td><td>Blackstone DocAI</td><td>16</td></tr><tr><td>3.6.1</td><td>Product Overview</td><td>16</td></tr><tr><td>3.6.2</td><td>Technology</td><td>16</td></tr><tr><td>3.6.3</td><td>Functionalities</td><td>16</td></tr><tr><td>3.6.4</td><td>Performance &amp; Impact</td><td>16</td></tr><tr><td>3.6.5</td><td>Our Review</td><td>17</td></tr><tr><td>3.7</td><td>Wells Fargo's Fargo Assistant</td><td>17</td></tr><tr><td>3.7.1</td><td>Product Overview</td><td>17</td></tr><tr><td>3.7.2</td><td>Technology</td><td>17</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>3.7.3</td>
      <td>Functionalities</td>
      <td>18</td>
    </tr>
    <tr>
      <td>3.7.4</td>
      <td>Performance &amp; Impact</td>
      <td>18</td>
    </tr>
    <tr>
      <td>3.7.5</td>
      <td>Our Review</td>
      <td>18</td>
    </tr>
    <tr>
      <td>3.8</td>
      <td>Goldman Sachs GS AI</td>
      <td>18</td>
    </tr>
    <tr>
      <td>3.8.1</td>
      <td>Product Overview</td>
      <td>18</td>
    </tr>
    <tr>
      <td>3.8.2</td>
      <td>Technology</td>
      <td>19</td>
    </tr>
    <tr>
      <td>3.8.3</td>
      <td>Functionalities</td>
      <td>19</td>
    </tr>
    <tr>
      <td>3.8.4</td>
      <td>Performance &amp; Impact</td>
      <td>20</td>
    </tr>
    <tr>
      <td>3.8.5</td>
      <td>Our Review</td>
      <td>20</td>
    </tr>
    <tr>
      <td>3.9</td>
      <td>S&amp;P Global Spark Assist</td>
      <td>21</td>
    </tr>
    <tr>
      <td>3.9.1</td>
      <td>Product Overview</td>
      <td>21</td>
    </tr>
    <tr>
      <td>3.9.2</td>
      <td>Technology</td>
      <td>21</td>
    </tr>
    <tr>
      <td>3.9.3</td>
      <td>Functionalities</td>
      <td>21</td>
    </tr>
    <tr>
      <td>3.9.4</td>
      <td>Performance &amp; Impact</td>
      <td>21</td>
    </tr>
    <tr>
      <td>3.9.5</td>
      <td>Our Review</td>
      <td>21</td>
    </tr>
    <tr>
      <td>3.10</td>
      <td>Balyasny BAMChatGPT</td>
      <td>22</td>
    </tr>
    <tr>
      <td>3.10.1</td>
      <td>Product Overview</td>
      <td>22</td>
    </tr>
    <tr>
      <td>3.10.2</td>
      <td>Technology</td>
      <td>22</td>
    </tr>
    <tr>
      <td>3.10.3</td>
      <td>Functionalities</td>
      <td>22</td>
    </tr>
    <tr>
      <td>3.10.4</td>
      <td>Performance &amp; Impact</td>
      <td>22</td>
    </tr>
    <tr>
      <td>3.10.5</td>
      <td>Our Review</td>
      <td>23</td>
    </tr>
    <tr>
      <td>3.11</td>
      <td>Man Group ManGPT</td>
      <td>23</td>
    </tr>
    <tr>
      <td>3.11.1</td>
      <td>Product Overview</td>
      <td>23</td>
    </tr>
    <tr>
      <td>3.11.2</td>
      <td>Technology</td>
      <td>23</td>
    </tr>
    <tr>
      <td>3.11.3</td>
      <td>Functionalities</td>
      <td>23</td>
    </tr>
    <tr>
      <td>3.11.4</td>
      <td>Performance &amp; Impact</td>
      <td>24</td>
    </tr>
    <tr>
      <td>3.11.5</td>
      <td>Our Review</td>
      <td>24</td>
    </tr>
    <tr>
      <td>3.12</td>
      <td>JPMorgan Chase LLM Suite</td>
      <td>24</td>
    </tr>
    <tr>
      <td>3.12.1</td>
      <td>Product Overview</td>
      <td>24</td>
    </tr>
    <tr>
      <td>3.12.2</td>
      <td>Technology</td>
      <td>25</td>
    </tr>
    <tr>
      <td>3.12.3</td>
      <td>Functionalities</td>
      <td>25</td>
    </tr>
    <tr>
      <td>3.12.4</td>
      <td>Impact</td>
      <td>25</td>
    </tr>
    <tr>
      <td>3.12.5</td>
      <td>Our Review</td>
      <td>26</td>
    </tr>
    <tr>
      <td>3.13</td>
      <td>Morgan Stanley Debrief</td>
      <td>26</td>
    </tr>
    <tr>
      <td>3.13.1</td>
      <td>Product Overview</td>
      <td>26</td>
    </tr>
    <tr>
      <td>3.13.2</td>
      <td>Technology</td>
      <td>26</td>
    </tr>
    <tr>
      <td>3.13.3</td>
      <td>Functionalities</td>
      <td>27</td>
    </tr>
    <tr>
      <td>3.13.4</td>
      <td>Performance &amp; Impact</td>
      <td>27</td>
    </tr>
    <tr>
      <td>3.13.5</td>
      <td>Our Review</td>
      <td>28</td>
    </tr>
    <tr>
      <td>3.14</td>
      <td>BOA Erica</td>
      <td>28</td>
    </tr>
  </tbody>
</table>
<table><tr><td>3.14.1 Product Overview</td><td>28</td></tr><tr><td>3.14.2 Technology</td><td>28</td></tr><tr><td>3.14.3 Functionalities</td><td>29</td></tr><tr><td>3.14.4 Impact</td><td>30</td></tr><tr><td>3.14.5 Our Review</td><td>30</td></tr><tr><td>3.15 High-Flyer DeepSeek R1</td><td>31</td></tr><tr><td>3.15.1 Product Overview</td><td>31</td></tr><tr><td>3.15.2 Technology</td><td>31</td></tr><tr><td>3.15.3 Functionalities</td><td>31</td></tr><tr><td>3.15.4 Performance &amp; Impact</td><td>31</td></tr><tr><td>3.15.5 Our Review</td><td>32</td></tr><tr><td>3.16 BlackRock Aladdin</td><td>32</td></tr><tr><td>3.16.1 Product Overview</td><td>32</td></tr><tr><td>3.16.2 Technology</td><td>32</td></tr><tr><td>3.16.3 Functionalities</td><td>33</td></tr><tr><td>3.16.4 Impact</td><td>34</td></tr><tr><td>3.16.5 Our Review</td><td>35</td></tr><tr><td>3.17 Citi Assist and Citi Stylus</td><td>36</td></tr><tr><td>3.17.1 Product Overview</td><td>36</td></tr><tr><td>3.17.2 Technology</td><td>37</td></tr><tr><td>3.17.3 Functionalities</td><td>37</td></tr><tr><td>3.17.4 Impact</td><td>38</td></tr><tr><td>3.17.5 Our Review</td><td>38</td></tr><tr><td>3.18 Kasisto KAI-GPT</td><td>38</td></tr><tr><td>3.18.1 Product Overview</td><td>38</td></tr><tr><td>3.18.2 Technology</td><td>38</td></tr><tr><td>3.18.3 Functionalities</td><td>39</td></tr><tr><td>3.18.4 Impact</td><td>39</td></tr><tr><td>3.18.5 Our Review</td><td>40</td></tr><tr><td>3.19 Boston Consulting Group Smart Banking AI</td><td>40</td></tr><tr><td>3.19.1 Product Overview</td><td>40</td></tr><tr><td>3.19.2 Technology</td><td>40</td></tr><tr><td>3.19.3 Functionalities</td><td>41</td></tr><tr><td>3.19.4 Impact</td><td>42</td></tr><tr><td>3.19.5 Our Review</td><td>42</td></tr><tr><td>3.20 RBC Borealis ATOM</td><td>43</td></tr><tr><td>3.20.1 Product Overview</td><td>43</td></tr><tr><td>3.20.2 Technology</td><td>44</td></tr><tr><td>3.20.3 Functionalities</td><td>44</td></tr><tr><td>3.20.4 Performance &amp; Impact</td><td>44</td></tr></table>
<table><tr><td>3.20.5</td><td>Our Review</td><td>44</td></tr><tr><td>3.21</td><td>Visa Account Attack Intelligence</td><td>45</td></tr><tr><td>3.21.1</td><td>Product Overview</td><td>45</td></tr><tr><td>3.21.2</td><td>Technology</td><td>45</td></tr><tr><td>3.21.3</td><td>Functionalities</td><td>46</td></tr><tr><td>3.21.4</td><td>Impact</td><td>46</td></tr><tr><td>3.21.5</td><td>Our Review</td><td>46</td></tr><tr><td>4</td><td>Conclusion</td><td>47</td></tr></table>
# 1 Introduction

Generative AI is rapidly reshaping the global business landscape, with breakthrough capabilities in language processing, content generation, and problem-solving [1, 2]. The financial sector, with its inherent technological sensitivity and data-driven nature, presents particularly fertile ground for generative AI applications [3, 4]. Major financial institutions are already deploying AI solutions across various functions, from market analysis and risk assessment to client service and knowledge management.

Despite the significant potential and growing adoption of generative AI in finance, there exists no comprehensive survey of real-world applications in this sector, as far as we know. While theoretical studies abound, practical applications and implementations across different regions and industry segments remain unexplored in academic literature. This paper addresses this gap by presenting a systematic analysis of deployed generative AI products in the financial sector, examining their technological foundations, practical applications, and measurable impacts.

Our study organizes these implementations both geographically and by industry segment, analyzing products from hedge funds, consulting firms, and banking institutions across North America, Europe, and Asia. For each product, we examine the underlying technology stack, key functionalities, and market impact, providing insights for institutions considering similar implementations. This practical focus on actual deployments offers valuable perspectives on how different regions and sectors approach generative AI adoption in finance.

# 2 Related Work

The development of large language models has significantly changed natural language processing (NLP). These models, such as GPT-4 [5] and the LLaMA series [6], are built on the transformer architecture. The transformer framework uses self-attention mechanisms, which allow for efficient text processing and generation. This approach improves contextual understanding and fluency. Unlike earlier architectures like Recurrent Neural Networks (RNNs) [7] and Long Short-Term Memory (LSTM) networks [8], transformers handle long-range dependencies better and support parallel processing.

LLMs have achieved remarkable progress in various NLP tasks. They have set new standards in machine translation, text generation, sentiment analysis, and language comprehension. For example, the BERT model [9], based on the transformer architecture, has improved the scalability and effectiveness of NLP systems through self-supervised pretraining. Generative models like the GPT series [10, 11]
<figure><img src="image_4.png" /><figcaption>Figure 1: Our categorization of organizations involved in finance Gen AI</figcaption></figure>
show strong capabilities in zero-shot and few-shot learning. These models can han-
dle diverse tasks without needing task-specific fine-tuning. This flexibility comes
from the large and varied datasets used during pretraining, which cover a wide
range of knowledge and skills.

A key achievement of LLMs is their ability to work with multiple languages. These models can process and generate text in many languages, including those with limited resources. Traditional translation methods rely on large amounts of parallel text, but LLMs benefit from extensive multilingual pretraining [6]. This allows them to generate coherent text even for underrepresented languages. This capability is important for addressing challenges related to low-resource languages, which often lack sufficient text data and specialized tools. By using the multilingual features of LLMs, researchers can better analyze and process these languages, helping to close gaps in linguistic research and promote inclusivity.

The use of LLMs in practical applications has gained significant attention. They are being applied in fields such as education, healthcare, and robotics [1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]. Their ability to understand and reason in a human-like way represents a major step toward Artificial General Intelligence (AGI). This progress is driving innovation and societal advancements across many areas [12, 27, 28, 29, 30]. Additionally, LLMs offer new opportunities to study underrepresented languages, helping to preserve and explore the cultural and intellectual richness of these languages.

Despite their impact in many fields, the use of LLMs in the financial sector is
still limited. While AI is deeply integrated into areas like healthcare and education,
there is little research summarizing the use of LLMs and advanced AI technologies
in finance. This gap highlights the need for a detailed overview, which this paper
aims to provide. By focusing on the applications and strategies of generative AI
in financial services, this work aims to show the potential of these technologies in
transforming the financial industry. It also seeks to lay the groundwork for future
advancements and wider adoption.

3 Gen AI Tools

3.1 Citadel LangCheck

3.1.1 Product Overview

Citadel AI's LangCheck is an open-source library designed to evaluate the quality and reliability of LLM applications [31]. The library provides a suite of tools that enable developers to assess various aspects of LLM-generated text, including factual consistency, fluency, toxicity, and sentiment. LangCheck is particularly useful for businesses that rely on third-party LLMs, as it allows them to validate and im-
prove the outputs of these models without needing to train their own foundation
models from scratch. The library is designed to be adaptable to various use cases,
such as monitoring anomalies in production, evaluating LLM-generated text, and
providing guardrails on outputs.

3.1.2 Technology

LangCheck employs a modular architecture built on Python, featuring customiz-
able evaluation metrics and visualization tools [32]. The library implements both
"offensive" and "defensive" approaches to LLM output quality control. The of-
fensive approach focuses on directly improving output quality through prompt
engineering and model fine-tuning, while the defensive approach validates and
suppresses problematic outputs. The system utilizes advanced NLP techniques
for text analysis, including semantic similarity measurements, named entity recog-
nition, and sentiment analysis algorithms. The library's evaluation framework is
designed to handle non-standardized input and output formats typical of LLM
applications, with support for both English and Japanese text processing.

3.1.3 Functionalities

LangCheck provides a comprehensive suite of tools designed to evaluate the quality and reliability of text generated by large language models [31]. One of its key features is the ability to perform similarity checks, which compare the generated text against ground truth data in the evaluation dataset. This ensures that the output aligns closely with expected results, which is particularly important in applications where accuracy is critical. Additionally, LangCheck includes functionality for factual consistency checks, which assess whether the generated text is consistent with known factual information. This helps to reduce the risk of misinformation, a significant concern when deploying LLMs in fields such as finance or healthcare.

Another important aspect of LangCheck is its schema validation capability [33]. For applications that require structured input and output, LangCheck ensures that the generated text adheres to predefined schemas. This is especially useful in scenarios where data must conform to specific formats, such as in legal documents or financial reports. Furthermore, LangCheck incorporates toxicity checks to identify harmful or discriminatory content in the generated text. This feature is crucial for maintaining ethical standards and avoiding potential reputational risks associated with inappropriate outputs.

LangCheck also evaluates the linguistic quality of the generated text through fluency checks. These checks assess grammatical correctness and vocabulary usage, ensuring that the output is linguistically sound and easy to understand. This is particularly valuable in customer-facing applications, where clear and coherent
communication is essential. Finally, LangCheck includes sentiment analysis capa-
bilities, which determine whether the generated text conveys a positive, negative,
or neutral sentiment. This functionality is highly relevant for applications in mar-
keting, customer service, and social media monitoring, where understanding the
emotional tone of text can provide valuable insights.

By combining these functionalities, LangCheck offers a robust framework for
evaluating LLM-generated text. It addresses multiple dimensions of text quality,
from factual accuracy and linguistic fluency to ethical considerations and emotional
tone. This makes LangCheck a versatile tool for developers and businesses seeking
to enhance the reliability and effectiveness of their LLM applications.

3.1.4 Impact

The introduction of LangCheck has significant implications for the development and deployment of LLM applications. By providing tools to evaluate and improve the quality of LLM-generated text, LangCheck helps businesses mitigate the risks associated with relying on third-party LLMs. This is particularly important in industries where accuracy and reliability are critical, such as finance, healthcare, and legal services. The library's ability to detect and suppress problematic outputs reduces the likelihood of errors or harmful content being disseminated, thereby enhancing the overall trustworthiness of LLM applications.

Moreover, LangCheck's open-source nature encourages collaboration and inno-
vation within the developer community. By making these tools freely available,
Citadel AI is contributing to the broader adoption of LLMs in various industries,
while also setting a standard for quality and reliability in LLM applications.

3.1.5 Our Review

LangCheck represents a significant step forward in the evaluation of LLM applica-
tions. The library's comprehensive suite of tools addresses many of the challenges
associated with using third-party LLMs, particularly in terms of output quality
and reliability. The ability to validate and suppress problematic outputs is espe-
cially valuable, as it helps businesses avoid potential pitfalls that could arise from
relying on unverified LLM-generated content.

However, while LangCheck provides a robust framework for evaluating LLM
applications, it is not without limitations. The library's effectiveness depends on
the quality of the evaluation datasets and the specific metrics used for validation.
In some cases, the predefined metrics may not fully capture the nuances of certain
use cases, requiring developers to customize or extend the library's functionalities.
Additionally, as LLMs continue to evolve, LangCheck will need to adapt to new
challenges and emerging use cases.
Overall, LangCheck is a valuable tool for businesses and developers working with LLMs. Its open-source nature, combined with its comprehensive evaluation capabilities, makes it a strong contender in the growing field of LLM application development. As the library continues to evolve, it has the potential to become an essential component of the LLM ecosystem, helping to ensure that LLM applications are both reliable and trustworthy.

## 3.2 Citadel Lens for LLMs

### 3.2.1 Product Overview

Lens for LLMs is Citadel AI's comprehensive evaluation platform designed to assess and improve LLM applications through a combination of automated and human feedback systems [34]. Currently in private beta, the platform addresses a fundamental challenge in LLM development: the lack of clear ground truth for evaluating model performance. While human evaluators are considered the gold standard, they are expensive and slow; automated evaluators are fast but may not accurately reflect human preferences. Lens bridges this gap by integrating both approaches.

### 3.2.2 Technology

The platform builds upon Citadel's open-source LangCheck library, incorporating its evaluation capabilities while adding sophisticated statistical analysis tools [32]. It employs multiple automated evaluators that work in parallel with human feedback collection mechanisms. The system includes advanced data processing pipelines that can handle evaluations in English, Japanese, Chinese, and German, with more languages planned for future releases. The technical architecture allows for seamless integration with various LLM APIs and RAG systems, enabling comprehensive testing across different model providers.

### 3.2.3 Functionalities

Lens provides a range of evaluation tools focused on practical business applications. The platform enables users to compare different LLM models (such as GPT-4 and Gemini) across various metrics, including factual consistency, answer relevance, and toxicity. Its data segmentation capabilities allow for detailed analysis of specific use cases, while built-in visualization tools help identify patterns and trends in model performance. The system includes efficient interfaces for collecting human feedback, automatically combining these insights with automated metrics to produce statistically significant comparisons.
### 3.2.4 Performance & Impact

The platform has demonstrated its effectiveness through rigorous testing and real-world applications. It successfully identifies statistically significant differences between model performances, providing confidence intervals that help users make informed decisions about model selection. The system's automated metrics show strong correlation with human preferences, validating its approach to combining different evaluation methods. The platform processes thousands of evaluations monthly, helping organizations maintain quality control over their LLM applications while reducing the cost and time typically associated with manual evaluation.

### 3.2.5 Our Review

A sophisticated evaluation platform that effectively merges automated metrics with human judgment, though its impact is currently limited by selective beta access. The platform shows promise in standardizing LLM evaluation practices across the industry.

## 3.3 McKinsey Lilli

### 3.3.1 Product Overview

Lilli is McKinsey's proprietary generative AI solution designed to streamline knowledge access and synthesis across the firm's vast intellectual property [35]. Launched in July 2023, the platform serves as a centralized knowledge hub, integrating over 40 curated sources containing more than 100,000 documents and interview transcripts, connecting experts across 70 countries.

### 3.3.2 Technology

The platform leverages advanced natural language processing and search capabilities to process both internal and third-party content. Built on industry-leading platforms, Lilli implements a dual-mode architecture: one for searching McKinsey's internal knowledge reserves and another for external sources. The system incorporates comprehensive security protocols and risk controls, ensuring secure data handling and compliance with firm-wide technology standards.

### 3.3.3 Functionalities

Lilli offers several key capabilities including rapid knowledge search and synthesis, identifying 5-7 most relevant content pieces for any query, summarizing key points, and expert identification. The platform functions as a thought-sparring partner,
helping prepare for meetings, anticipating potential questions, and connecting dif-
ferent knowledge areas. It enables users to find analogous cases and comparable
examples across industries, significantly reducing research time.

3.3.4 Performance & Impact

Since its launch, Lilli has achieved 72 percent firm-wide adoption, with users re-
porting up to 30 percent time savings in knowledge search and synthesis tasks.
The platform processes over 500,000 prompts monthly, demonstrating significant
impact on workflow efficiency. Users particularly benefit from reduced meeting
preparation time (up to 20 percent savings) and improved quality of expertise
contributions. The platform has influenced how McKinsey creates and codifies
knowledge, reshaping internal workflows.

3.3.5 Our Review

A robust enterprise knowledge assistant that effectively combines search capa-
bilities with AI-driven insights, though currently limited to internal use within
McKinsey. The platform demonstrates strong potential for enterprise knowledge
management applications.

3.4 BloombergGPT

3.4.1 Product Overview

BloombergGPT [36] is a domain-specific generative AI solution designed to address the unique challenges of NLP in the financial industry. Developed by Bloomberg, the model is built on a 50-billion parameter architecture and trained on a blend of financial and general-purpose datasets, making it one of the most specialized LLMs in the financial domain. Leveraging over four decades of curated financial data, BloombergGPT supports a diverse range of tasks, including sentiment analysis, named entity recognition (NER), and question answering, while maintaining competitive performance on general LLM benchmarks.

3.4.2 Technology

The model employs a decoder-only transformer architecture inspired by BLOOM
and optimized for large-scale data processing. BloombergGPT's training utilized
a mixed dataset of 363 billion tokens from financial sources such as news, company
filings, and press releases, combined with 345 billion tokens from general datasets.
To ensure precision and efficiency, the model features a custom Unigram tokenizer
tailored for financial language, enabling nuanced tokenization of complex financial
terms [37]. The training was conducted on 512 NVIDIA A100 GPUs over 64 nodes, leveraging advanced parallelization techniques and mixed precision to maximize hardware utilization.

### 3.4.3 Functionalities

BloombergGPT excels in finance-specific NLP tasks, including financial sentiment analysis, trend detection, and conversational query resolution. It can identify entities, relationships, and trends from structured and unstructured data, offering high accuracy in sentiment classification and numerical reasoning. Additionally, the model facilitates report summarization, risk assessment, and contextual insights for financial professionals. BloombergGPT also integrates seamlessly into Bloomberg's existing platforms, enhancing the workflow of analysts and investors by providing precise and actionable insights.

### 3.4.4 Performance & Impact

Since its inception, BloombergGPT has demonstrated remarkable performance across financial and general-purpose benchmarks. It consistently outperforms comparable models in sentiment analysis and numerical reasoning tasks, achieving a win rate of 93% on domain-specific evaluations. The model processes up to 569 billion tokens during training, enabling it to deliver high accuracy and reliability in diverse financial applications. By reducing the time required for data analysis and increasing the accuracy of predictions, BloombergGPT has significantly enhanced decision-making efficiency for users.

### 3.4.5 Our Review

BloombergGPT stands out as a groundbreaking application of generative AI in the financial domain. Its hybrid training approach, blending domain-specific expertise with general NLP capabilities, positions it as a highly versatile tool for financial professionals. While its current applications are primarily tailored for Bloomberg's ecosystem, the model showcases immense potential for broader applications in financial analytics and decision support.

## 3.5 TIFIN Sage

### 3.5.1 Product Overview

TIFIN Sage is an AI-powered investment assistant functioning as a personalized CIO for financial advisors [38]. It focuses on rapid portfolio analysis and actionable insights for both client and prospect portfolios. The platform emerged as a solution
to meet growing demands for AI-driven wealth management tools, with industry
surveys showing 90% of advisors believe AI can grow their business by over 20%.

3.5.2 Technology

The core technology stack of TIFIN Sage [38] is built around advanced generative AI capabilities. At its heart are large language models that power the conversational interface, enabling natural dialogue between advisors and the system. The platform employs sophisticated Natural Language Processing for interpreting client communications and financial data. Real-time data processing engines analyze market conditions, while machine learning algorithms handle portfolio optimization. Neural networks work continuously to identify patterns in market trends, and all of this is connected through a secure API infrastructure that ensures safe financial data integration.

3.5.3 Functionalities

TIFIN Sage's functionality is centered around two main components [39]. The Conversational Investment Co-Pilot leverages LLM technology to facilitate natural investment queries, providing real-time market analysis and continuous portfolio monitoring while supporting investment decisions through AI-driven insights. The Next Best Actions system generates client-specific recommendations by analyzing market trends, identifying growth opportunities, and suggesting engagement strategies. These components work in tandem to create a comprehensive advisory support system.

3.5.4 Performance & Impact

TIFIN Sage has demonstrated significant impact in wealth management opera-
tions. The platform has achieved a 20% reduction in compliance costs while 80%
of advisors report improved ability to derive actionable insights from client data.
Users consistently report increased operational efficiency through automation of
routine tasks, enhanced client personalization capabilities, and substantial reduc-
tions in time spent on manual analysis. These improvements have translated into
stronger client relationships and more efficient portfolio management.

**3.5.5 Our Review**

TIFIN Sage represents a sophisticated integration of generative AI technology with wealth management expertise. The platform's ability to translate complex financial data into actionable insights through natural language interaction sets it apart in the market. While the technology shows impressive capabilities in personalized
client engagement and automated portfolio analysis, potential users should carefully consider implementation costs, training requirements, and integration needs. The platform's success in combining advanced AI capabilities with practical financial applications makes it a noteworthy solution in the evolving landscape of AI-powered wealth management tools.

## 3.6 Blackstone DocAI

### 3.6.1 Product Overview

DocAI is Blackstone's proprietary generative AI tool, developed over a 10-month period to enhance internal search and document analysis capabilities across the organization [40, 41, 42]. The platform is designed to serve Blackstone's 5,000-strong workforce by providing sophisticated search and summarization features for both internal and external documents. Set to launch firm-wide in fall 2024, the system represents Blackstone's strategic investment in AI technology to improve operational efficiency.

### 3.6.2 Technology

The platform utilizes generative AI models specifically designed to analyze and process financial and investment-related documentation. Unlike general-purpose AI search tools, DocAI is built to handle specialized content ranging from proprietary Blackstone deal information to external consulting research and investment bank analyses. The system employs advanced document processing capabilities that enable both granular information retrieval and comprehensive summarization features.

### 3.6.3 Functionalities

DocAI serves as a comprehensive document management and analysis platform, allowing users to upload, search, and analyze various types of documents relevant to their work [40]. The system provides personalized experiences where users can create customized views of information and set priorities based on their specific roles. It supports various use cases, from deal professionals analyzing sector trends to software engineers searching design documents, with particular emphasis on retrieving specific topical documentation and creating targeted summaries.

### 3.6.4 Performance & Impact

Early pilot programs have demonstrated DocAI's effectiveness across multiple departments [40]. Real estate and private equity teams have successfully utilized
the platform for deal research, while legal and compliance teams have employed
it for tracking credit covenants and mandates. The technology team has found
value in using the system to understand system architectures and connections.
The platform's ability to save significant time in information retrieval and analysis
has been noted across these different use cases.

### 3.6.5 Our Review

DocAI represents a sophisticated approach to solving the persistent challenge of enterprise search in financial institutions. Its focus on personalization and curated content sets it apart from general-purpose AI search tools, while its ability to handle specialized financial documentation addresses a crucial need in the industry. The platform's development reflects Blackstone's understanding that effective data management and retrieval are crucial for maintaining competitive advantage in private equity. While still in its early stages of full deployment, DocAI's successful pilot programs and planned firm-wide rollout suggest a well-executed implementation of AI technology in financial services.

## 3.7 Wells Fargo's Fargo Assistant

### 3.7.1 Product Overview

Fargo is Wells Fargo's AI-powered virtual banking assistant, launched in March 2023 and built in collaboration with Google. The assistant has demonstrated significant traction with 20 million customer interactions since launch, projecting to reach 100 million annual interactions [43]. As one of the few major banks moving beyond proof-of-concept stage in generative AI deployment, Wells Fargo has positioned Fargo as a core component of its digital banking strategy.

### 3.7.2 Technology

Fargo's technology stack is built on Wells Fargo's proprietary Tachyon platform, which manages multiple AI models and cloud services [44]. The assistant initially launched using Google's PaLM 2 LLM through Dialogflow but has evolved to incorporate multiple LLMs for different tasks. The platform also integrates Meta's Llama 2 for specific internal functions. This multi-model approach allows for specialized handling of various banking tasks, while the Tachyon platform provides the necessary infrastructure for model sharding, tensor sharding, and resilient performance.
### 3.7.3 Functionalities

The virtual assistant handles a range of everyday banking tasks through both voice and text interfaces. Core functions include bill payments, money transfers, transaction details, spending insights, and account management. Fargo maintains an engagement rate of 2.7 interactions per session, indicating strong user adoption [45]. The platform also integrates with Livesync, a complementary application providing financial planning and goal-setting advice, which attracted one million monthly active users in its first month.

### 3.7.4 Performance & Impact

Fargo's rapid adoption and scaling demonstrate its effectiveness in digital banking transformation. The platform has successfully processed 20 million interactions since launch, with projections indicating five-fold growth potential. Wells Fargo has supported this initiative by training 4,000 employees through Stanford's Human-centered AI program, indicating a comprehensive approach to AI integration. The assistant's success has also led to expansion plans for small business customers and wealth management clients.

### 3.7.5 Our Review

Fargo represents one of the most ambitious and successful deployments of generative AI in retail banking. The platform's sophisticated multi-model approach and integration with the Tachyon infrastructure demonstrate Wells Fargo's commitment to robust AI implementation. While the technology shows impressive capabilities, particularly in handling everyday banking tasks, the bank's focus on multimodal interactions and "input multimodality" suggests future enhancements to create more intuitive and comprehensive banking experiences. The platform's success in navigating regulatory requirements while maintaining rapid innovation makes it a significant case study in enterprise AI deployment.

## 3.8 Goldman Sachs GS AI

### 3.8.1 Product Overview

Goldman Sachs launched its **GS AI Platform Strategy** in 2024 as a centralized infrastructure to integrate and manage generative AI models across the organization [46, 47, 48]. The platform is designed to support a range of enterprise applications, from financial modeling to workflow automation. It focuses on providing a secure and compliant environment for deploying both foundational and
fine-tuned models, enabling the development of advanced AI-driven tools tailored
for code generation, internal and client-facing operations.

The **GS AI Assistant** is a conversational AI tool introduced to transform financial services by offering real-time, human-like interactions. Acting as a virtual financial advisor, it assists with tasks such as code translation, email summarization, and streamlining communication. Its advanced natural language processing capabilities allow it to respond to complex queries, provide detailed insights, and automate routine operations, making it a valuable tool for employees. Additionally, tasks like portfolio analysis, investment recommendations, and risk assessment are managed by separate AI-driven tools developed by Goldman Sachs, which analyze large datasets to enhance investment decision-making.

3.8.2 Technology

The GS AI Platform utilizes advanced LLMs and RAG techniques to process data
from both internal and external sources [49]. It is designed with a poly cloud infras-
tructure, leveraging different cloud providers for various AI workloads. According
to the chief information officer, the platform can utilize large foundational models
such as GPT-4 and Gemini for reasoning tasks while also supporting fine-tuned
open-source models for domain-specific applications. It adheres to Goldman Sachs'
enterprise-wide security and compliance protocols, ensuring the safe deployment
of AI in financial operations.

The GS AI Assistant incorporates natural language processing and machine
learning to provide real-time conversational support. It securely connects with
proprietary systems and external financial datasets, ensuring seamless data pro-
cessing. The assistant follows a modular design, enabling flexible deployment
across different applications while maintaining strong data protection measures.
Fine-tuning techniques optimize its performance for Goldman Sachs-specific do-
mains, including proprietary technologies like Slang.

3.8.3 Functionalities

The GS AI Platform, GS AI Assistant, and other AI-driven tools collectively enhance financial operations by streamlining workflows, automating tasks, and providing real-time insights [45], enabling more efficient and informed decision-making across the organization.

These tools integrate foundational and fine-tuned models to manage com-
plex workflows for hypothesis testing in macroeconomic and market scenarios.
They provide sentiment analysis and forecasting capabilities, supporting financial
decision-making by analyzing market trends. The platform automates manual
processes such as document summarization and report generation, reducing time
spent on repetitive activities and boosting productivity. It also enables conversa-
tional support, allowing for natural, real-time interactions that assist in financial
guidance and operational tasks. Furthermore, these tools enhance task automation
by handling meeting briefings, summarizing reports and emails, translating code,
and managing client communications, significantly improving workflow efficiency.

3.8.4 Performance & Impact

Goldman Sachs' generative AI products, including the GS AI Platform and GS AI Assistant, have driven significant advancements in productivity, efficiency, and decision-making throughout the organization. Goldman Sachs has implemented the GS AI Platform across the firm, providing a centralized framework for integrating generative AI into daily operations. This platform supports developers with AI-assisted coding tools, leading to a reported 20% increase in productivity for developers. By automating repetitive tasks such as summarizing emails, generating reports, and translating code, the GS AI Assistant allows employees to focus on higher-value work. This has reduced the manual workload for over 10,000 employees, with plans to expand its availability across all knowledge workers. The AI Assistant enhances client-facing services by offering immediate responses to inquiries efficiently. It also fosters trust among employees by providing support in areas like meeting preparation and financial planning, without replacing their roles. The GS AI Platform is designed to align with industry security and compliance standards to handle proprietary data safely, though specific measures implemented by Goldman Sachs are not publicly detailed. Through the strategic deployment of the GS AI Platform and Assistant, Goldman Sachs has reinforced its leadership in financial innovation. By aligning these tools with employee needs and organizational goals, the firm is setting a benchmark for the effective use of generative AI in the financial sector.

3.8.5 Our Review

Goldman Sachs efficiently deploys generative AI through the AI Platform and AI Assistant, balancing innovation with practical application. These products facilitate domain-specific modeling and optimize routine tasks. The company emphasizes responsible AI adoption, prioritizing data security, regulatory compliance, and employee trust. By aligning advanced technology with practical needs, Goldman Sachs sets a grounded example for effective AI implementation in the financial sector.
## 3.9 S&P Global Spark Assist

### 3.9.1 Product Overview

Spark Assist is S&P Global's proprietary generative AI-based copilot, developed jointly between S&P's AI-focused subsidiary Kensho and internal technology teams [50]. Launched in Q1 2024 as part of the company's "enterprise-wide strategic focus" on AI, Spark Assist represents S&P's commitment to embedding AI throughout its operations. The platform is notable for being developed entirely in-house, without relying on third-party vendors for its chat interface.

### 3.9.2 Technology

The platform is built using large language models enriched with S&P's proprietary data, creating a domain-specific AI assistant for financial services [50, 51]. Running entirely on internal infrastructure, Spark Assist exemplifies S&P's strategy of maintaining control over its AI technology stack. The platform is remarkably cost-efficient, operating at less than $1 per user per month, demonstrating effective scalability in enterprise AI deployment.

### 3.9.3 Functionalities

Spark Assist serves as a digital personal assistant, helping users navigate AI models using large datasets specific to financial services [50, 52]. The platform assists with research, coding, and sentiment analysis tasks, focusing on reducing time spent on routine activities. It integrates with S&P's Market Intelligence tools and the company's Marketplace platform, which features genAI search capabilities for browsing datasets across S&P's business units.

### 3.9.4 Performance & Impact

The platform's implementation is supported by a comprehensive training program developed in partnership with Accenture, targeting all 35,000 S&P Global employees [51]. Early results show significant productivity gains, with employees reporting substantial time savings on routine tasks. The platform's success has led to its widespread deployment throughout the organization, with plans to expand use cases and capabilities.

### 3.9.5 Our Review

Spark Assist represents a significant achievement in enterprise AI deployment, particularly notable for its fully internal development approach. The platform's integration with S&P's vast financial data resources and its cost-effective deployment
model demonstrate the potential for large organizations to successfully develop and scale proprietary AI solutions. While still in its early stages, the combination of comprehensive employee training and practical applications suggests a well-thought-out strategy for AI adoption in financial services. The platform's focus on specific financial use cases, rather than general-purpose capabilities, aligns well with S&P's core business needs and regulatory requirements.

## 3.10 Balyasny BAMChatGPT

### 3.10.1 Product Overview

BAMChatGPT is Balyasny Asset Management's specialized AI chatbot, developed by their in-house applied AI research team staffed with former Google and DeepMind talent [53, 54]. The platform, led by ex-Google data scientist Charlie Flanagan, has achieved widespread adoption with 80% of the fund's employees utilizing the tools. The system represents a focused effort to create financial-specific AI solutions that outperform general-purpose models in specialized applications.

### 3.10.2 Technology

The platform's core technology is built around retrieval-augmented generation and their proprietary BAM Embeddings system. Unlike general-purpose AI models, BAMChatGPT uses the Mistral 7B Instruct model [55] enhanced with specialized embeddings designed specifically for financial terminology and contexts. The system incorporates external data sources through RAG, allowing it to process recent financial reports and news while maintaining accuracy in financial contexts.

### 3.10.3 Functionalities

BAMChatGPT serves as an advanced financial analysis tool, helping traders answer complex questions about specific stocks and assess global events' impact on portfolios. The platform specializes in processing financial jargon and sector-specific terminology, providing more accurate and relevant responses for financial applications. Through BAM Embeddings, the system can effectively navigate and analyze financial documents, offering superior contextual understanding compared to general-purpose AI solutions.

### 3.10.4 Performance & Impact

The platform has demonstrated significant performance advantages over general-purpose AI systems in financial applications. Internal tests show BAM Embeddings achieving 60% accuracy in retrieving relevant financial document passages,
compared to OpenAI's sub-40% performance. On the FinanceBench system, BAM
Embeddings scored 55% accuracy versus OpenAI's 47% [53, 54]. However, the
system still faces challenges with a 30% error rate and potential hallucinations,
comparable to industry standards.

### 3.10.5 Our Review

BAMChatGPT represents a significant advancement in specialized AI for finan-
cial services, particularly notable for its superior performance in financial contexts
compared to general-purpose AI systems. The platform's success in embedding
financial terminology and context demonstrates the value of domain-specific AI
development. While the system still faces common AI challenges like halluci-
nations, its improved accuracy in financial applications and widespread internal
adoption suggest a successful approach to integrating AI in investment manage-
ment. The combination of proprietary embeddings and RAG technology positions
BAMChatGPT as a leading example of specialized AI tools in the financial sector.

## 3.11 Man Group ManGPT

3.11.1 Product Overview

ManGPT is Man Group's internally developed AI portal that provides controlled access to OpenAI's GPT model through Microsoft's interface [56, 57]. Launched in 2023, the platform represents Man Group's strategic approach to leveraging advanced language models while maintaining security and control. The system has seen rapid adoption across the organization, with usage metrics showing consistent growth in users, messages, and conversations since its May 2023 launch.

3.11.2 Technology

The platform integrates OpenAI's GPT model through Microsoft's interface, with added security and control layers specific to Man Group's requirements. The technology builds upon Man Group's long-standing experience with large language models, representing what CTO Gary Collier describes as a "large step forward" in sophistication [56]. The implementation is supported by Man Group's substantial technology team of over 500 people, who ensure the platform's security and effectiveness while maintaining compliance with financial industry standards.

3.11.3 Functionalities

ManGPT serves multiple purposes within Man Group's operations, enabling pro-
fessionals across the organization to engage with AI for idea formation and de-
velopment. The platform facilitates information management and summarization
tasks, providing interactive assistance through a controlled environment. This sys-
tematic approach to AI access ensures that advanced capabilities are available to
all employees while maintaining necessary security protocols. The system has been
designed to support various use cases across the organization, from research and
analysis to operational efficiency improvements.

3.11.4 Performance & Impact

According to Man Group's internal data, ManGPT has achieved remarkable adop-
tion rates within the organization, reaching approximately 70% of total employees
by early 2024 [58]. The platform has successfully processed over 350,000 messages
and facilitated more than 60,000 conversations, demonstrating its integral role in
daily operations. This level of engagement indicates strong integration into the
firm's workflow processes and suggests significant impact on operational efficiency
and idea generation capabilities.

3.11.5 Our Review

ManGPT represents a thoughtful approach to implementing generative AI in a highly regulated financial environment. The platform's controlled access model and steady adoption rates suggest successful integration of AI technology while maintaining necessary security and compliance standards. While built on existing technology, Man Group's implementation demonstrates how financial institutions can effectively adapt consumer AI technology for professional use. The platform's success in attracting consistent user growth while maintaining security controls makes it a notable example of enterprise AI deployment in the investment management sector, particularly in its ability to balance innovation with regulatory requirements and operational security needs.

## 3.12 JPMorgan Chase LLM Suite

3.12.1 Product Overview

JPMorgan Chase has partnered with OpenAI to introduce a tool kit called "LLM Suite" to its internal employees [59]. LLM Suite can help generate and refine written documents, summarize large volumes of information, and offer creative proposals, thereby streamlining the flow of information within the company. This AI assistant is already assisting over 60,000 employees.
### 3.12.2 Technology

JPMorgan Chase took a strategic approach by designing LLM Suite as an enterprise portal architecture rather than developing proprietary language models. The system functions as an orchestration layer that can integrate and manage access to external LLMs, initially launching with OpenAI's models while maintaining the technical flexibility to incorporate other providers' models. By implementing this portal design pattern, JPMorgan created a secure wrapper around external AI services that protects their proprietary data while allowing employees to leverage state-of-the-art language models for tasks like email composition and report writing. This architecture enables the bank to maintain vendor independence and adapt to advances in Gen AI while keeping their sensitive data siloed from external training.

### 3.12.3 Functionalities

The LLM Suite offers several essential features that establish it as a vital resource for employees. It enhances the efficiency of content creation through its writing support capabilities, enabling employees to generate and refine written documents with ease. This functionality not only reduces the time required for drafting reports but also improves the quality of outputs. Additionally, the suite fosters creative thinking by supporting brainstorming sessions and generating innovative ideas, encouraging employees to engage in critical thinking and drive organizational growth.

Another key feature is document summarization, where the LLM Suite leverages LLM to condense extensive documents, streamlining knowledge dissemination and ensuring that crucial information remains accessible for informed decision-making at all organizational levels. Furthermore, the suite provides coding solutions that assist in data analysis by offering suggestions and automating tasks such as Excel data processing, allowing employees to perform analyses more quickly and efficiently.

### 3.12.4 Impact

JPMorgan Chase has long placed significant emphasis on traditional artificial intelligence and machine learning, continuously investing in these areas. However, the emergence of LLMs such as ChatGPT, with their extraordinary capabilities, has compelled the organization to pivot and adapt to this transformative technology. The launch of "LLM Suite" marks a significant step for JPMorgan Chase in the field of LLMs and symbolizes the financial industry's broader embrace of the AI-driven transformation.
### 3.12.5 Our Review

JPMorgan Chase, the largest bank in the United States by assets, had previously imposed a ban restricting its employees from using ChatGPT for over a year due to concerns regarding security and privacy. Now, with the introduction of LLM Suite, which has received approval from the bank, it is evident that the financial industry is poised to fully harness the potential of large language models in the near future while maintaining robust data protection measures. And the inherent limitations of large language models, such as deployment challenges and data privacy concerns, remain critical issues that warrant our attention.

## 3.13 Morgan Stanley Debrief

### 3.13.1 Product Overview

Morgan Stanley Debrief is an innovative generative AI-powered application designed to optimize the workflow of financial advisors by automating the labor-intensive tasks associated with client meetings [60]. This tool is specifically tailored to enhance the productivity of advisors, enabling them to focus more on strategic decision-making and less on administrative burdens [61]. With client consent, Debrief automates meeting transcription, summarizes key discussion points, identifies follow-up actions, and drafts personalized emails for advisors to send post-meeting [62]. By integrating these functionalities, Debrief ensures that advisors maintain a high standard of client service while saving valuable time. This product reflects Morgan Stanley's broader commitment to leveraging AI technologies to improve operational efficiency and client engagement [63, 64].

### 3.13.2 Technology

The technology behind Morgan Stanley Debrief is built on OpenAI's advanced generative AI framework, complemented by robust NLP capabilities [64]. This combination allows the tool to perform real-time transcription and produce semantically accurate summaries of client meetings [62]. The system uses proprietary algorithms to extract actionable insights from conversations, identifying follow-up tasks and aligning them with client goals [60]. Furthermore, Debrief includes an email drafting feature powered by contextual language modeling, which tailors communications to reflect the unique nuances of each client interaction.

Morgan Stanley has embedded Debrief within its secure, proprietary infrastructure to ensure compliance with financial industry regulations and data privacy standards [61]. The system is equipped with encryption protocols and operates in a closed ecosystem to safeguard sensitive client information. Additionally, the AI
engine is trained on financial-specific data, enabling it to generate outputs that align with the firm's standards for professionalism and accuracy [63].

### 3.13.3 Functionalities

Morgan Stanley Debrief offers a comprehensive set of functionalities designed to support financial advisors throughout the entire client interaction lifecycle [60]. The tool automatically transcribes client meetings with remarkable accuracy, capturing every detail with client consent. Once the transcription is complete, Debrief generates a concise summary highlighting key discussion points and areas requiring follow-up. This ensures that no critical information is overlooked.

One of its standout features is the identification of actionable insights. By analyzing the conversation context, Debrief flags items that require immediate attention or further discussion, helping advisors prioritize tasks effectively. The tool also drafts personalized follow-up emails based on the meeting's content. These emails are crafted to reflect the specific tone and context of the discussion, ensuring that communications remain professional and client-centered.

In addition to these features, Debrief integrates seamlessly with Morgan Stanley's internal systems, enabling advisors to retrieve meeting records and action items effortlessly. This seamless integration supports better time management and enhances the advisor's ability to deliver consistent and high-quality service.

### 3.13.4 Performance & Impact

Since its rollout, Morgan Stanley Debrief has had a transformative impact on the workflows of financial advisors. By automating tasks such as note-taking, summarization, and follow-up communication, the tool has enabled advisors to reclaim valuable time. For example, advisors have reported saving up to 30 minutes per meeting, allowing them to focus more on understanding client needs, analyzing financial strategies, and fostering deeper relationships.

The tool's automation capabilities have also enhanced the consistency and accuracy of client records, minimizing the risk of human error in meeting documentation. Clients benefit from faster and more tailored communication, which enhances their overall experience and trust in Morgan Stanley's services. Additionally, the ability to prioritize follow-ups and actionable insights ensures that advisors can address client needs proactively, further improving client satisfaction and retention rates.

Internally, the adoption of Debrief has showcased Morgan Stanley's leadership in leveraging generative AI to enhance productivity. The tool has reduced administrative workloads, allowing advisors to serve more clients effectively without compromising service quality. As a result, Morgan Stanley has not only improved
operational efficiency but has also strengthened its reputation as an innovator in
financial technology.

### 3.13.5 Our Review

Morgan Stanley Debrief is a great example of how generative AI can be effec-
tively applied to solve industry-specific challenges in financial services. The tool
addresses the long-standing issue of time-consuming administrative tasks, allowing
financial advisors to focus more on strategic activities and less on logistical ones.
Its ability to transcribe, summarize, and generate follow-up communications in
real-time is a testament to the capabilities of modern generative AI systems.

The seamless integration of Debrief within Morgan Stanley's secure infrastruc-
ture highlights the firm's commitment to maintaining the highest standards of
data privacy and regulatory compliance. However, the tool's success depends on
the continuous refinement of its AI models and its ability to adapt to diverse client
scenarios.

Overall, Debrief has the potential to set a new standard for AI-driven produc-
tivity tools in the financial sector. By enhancing advisor efficiency and improving
the client experience, Morgan Stanley is well-positioned to maintain its compet-
itive edge in an increasingly technology-driven industry. As Debrief continues to
evolve, it will likely serve as a benchmark for other financial institutions looking
to implement generative AI solutions.

## 3.14 BOA Erica

### 3.14.1 Product Overview

Erica is Bank of America's AI-powered virtual financial assistant, serving as the cornerstone of their digital transformation strategy [65]. The platform delivers personalized banking experiences through AI-driven interactions, combining automated assistance with proactive financial guidance [65]. Erica represents BofA's commitment to modernizing banking services while maintaining personalized customer relationships.

### 3.14.2 Technology

Erica's technical foundation rests on several interconnected AI technologies. At its core, NLP powers the platform's ability to understand both written and spoken commands, enabling natural conversations with users [66]. This capability works alongside a sophisticated chatbot framework that manages real-time interactions while maintaining conversational fluidity.
The system's intelligence grows through continuous learning. Machine learning algorithms analyze millions of customer interactions to improve response accuracy, while predictive analytics examines historical data to anticipate customer needs and offer timely financial recommendations.

Personalization lies at the heart of Erica's effectiveness. The platform processes individual user data from transaction patterns to financial objectives to provide customized guidance. To ensure security, the system employs robust protection measures, including biometric verification through voice and fingerprint recognition, coupled with behavioral pattern analysis.

Recent upgrades to Erica have incorporated LLM technology, though Bank of America maintains privacy around specific implementation details. This addition has enhanced the platform's ability to understand context and generate more nuanced responses.

### 3.14.3 Functionalities

Erica excels in everyday banking through its advanced conversational interface. Users can perform common tasks like checking balances, paying bills, and managing accounts using simple voice or text commands. The platform's intelligent transaction analysis automatically categorizes spending and provides budget optimization insights, making financial management more intuitive.

Building on this foundation, Erica takes a proactive approach to financial services. The platform has generated impressive results, delivering 60 million "Spend Path" insights and 37 million proactive notifications to customers. These alerts cover various aspects of financial management, from flagging unusual account activity to tracking subscription changes and identifying merchant refunds. A notable achievement is the delivery of over 4 million notifications about Preferred Rewards eligibility, demonstrating the system's ability to identify valuable opportunities for customers.

Building on this foundation, Erica takes a proactive approach to financial services. The platform has generated impressive results, delivering 60 million "Spend Path" insights and 37 million proactive notifications to customers. These alerts cover various aspects of financial management, from flagging unusual account activity to tracking subscription changes and identifying merchant refunds. A notable achievement is the delivery of over 4 million notifications about Preferred Rewards eligibility, demonstrating the system's ability to identify valuable opportunities for customers [67].

The platform's integration capabilities further enhance its utility. Through seamless connection with Merrill Edge [68], Erica provides comprehensive investment services access, supporting both self-directed investors and those seeking guided assistance. For more complex matters, the system smoothly transitions users to live chat support, with plans to implement direct connections to product
specialists, ensuring comprehensive customer service coverage.

### 3.14.4 Impact

Erica has demonstrated remarkable impact through its digital banking transformation. The success of the platform comes from its extensive banking capabilities. Through natural language processing, Erica handles everyday banking tasks from balance inquiries to fund transfers, while automatically categorizing transactions for better budget management. The system has proven highly proactive, delivering 60 million "Spend Path" insights and 37 million notifications covering account activities, unusual transactions, and subscription changes [69]. Additionally, it has sent 4 million notifications about Preferred Rewards eligibility.

This digital transformation has significantly improved operational efficiency. While Erica's automation has reduced the need for routine branch visits, Bank of America has simultaneously modernized 3,700 financial centers, maintaining a balance between digital convenience and personal service. The platform's integration with Merrill Edge further enhances its utility, offering both self-directed and guided investing options, with plans to implement direct connections to product specialists for complex inquiries.

### 3.14.5 Our Review

Erica represents a landmark achievement in banking AI integration. Its success is evident through high customer satisfaction rates and management of substantial assets, while maintaining personalized service across millions of interactions. The platform's sophisticated AI architecture proves that large-scale automation can coexist with individualized banking services.

The system excels in several key areas: generating proactive financial insights, seamlessly integrating investment services, and attracting younger customers. These strengths, combined with ongoing LLM integration, position Erica at the forefront of banking innovation. While there's room to expand specialist services and deepen investment advisory capabilities, these improvements are already under development.

Bank of America's strategic approach balances technological advancement with human interaction, exemplifying their "high-tech, high-touch" philosophy. Erica's achievements demonstrate how AI can transform banking while maintaining personal service quality, setting new standards for financial technology implementation.
3.15 High-Flyer DeepSeek R1

3.15.1 Product Overview

DeepSeek R1 is a revolutionary AI model developed by DeepSeek AI, a division of the quantitative trading firm High-Flyer [70]. The model focuses on enhancing reasoning capabilities through reinforcement learning and comes in two versions - R1-Zero, which uses pure reinforcement learning without supervised fine-tuning, and the more advanced R1 which incorporates multi-stage training and cold-start data. The platform demonstrates remarkable capabilities in mathematical reasoning, coding, and knowledge-based tasks, achieving performance comparable to OpenAI's o1-1217 on various benchmarks [71].

3.15.2 Technology

DeepSeek R1's architecture is built using an innovative approach to reinforcement learning. The platform utilizes DeepSeek-V3-Base as its foundation and employs the GRPO (Group Relative Policy Optimization) framework for training. The model architecture demonstrates impressive efficiency, with R1-Zero naturally developing sophisticated reasoning behaviors through pure reinforcement learning. For R1, the technology incorporates a multi-stage training pipeline that includes cold-start data, reasoning-oriented reinforcement learning, rejection sampling, and comprehensive fine-tuning.

3.15.3 Functionalities

The model excels across a wide range of tasks, particularly in areas requiring complex reasoning. On mathematical challenges like AIME 2024, it achieves a 79.8% pass rate at first attempt, matching or exceeding OpenAI's o1-1217. In coding competitions, it demonstrates expert-level performance with a 96.3 percentile rating on Codeforces. The platform also shows strong capabilities in knowledge-based tasks, achieving 90.8% accuracy on MMLU and 71.5% on GPQA Diamond. Additionally, it performs exceptionally well in creative writing and general question answering, with an 87.6% win rate on AlpacaEval 2.0.

3.15.4 Performance & Impact

DeepSeek R1's performance metrics are impressive across multiple domains. In mathematical reasoning, it achieves 97.3% accuracy on MATH-500. The platform demonstrates significant improvements over its predecessor DeepSeek-V3, particularly in STEM-related questions. Its impact extends beyond traditional benchmarks, with strong performance in document analysis, factual queries, and
instruction following. The model has also been successfully distilled into smaller,
more efficient versions, with even the 14B parameter version outperforming many
larger models.

3.15.5 Our Review

DeepSeek R1 represents a significant advancement in AI reasoning capabilities, particularly notable for its innovative use of reinforcement learning. The platform's ability to achieve state-of-the-art performance across diverse tasks while maintaining readable and coherent outputs makes it a standout solution in the field. While there are some limitations, such as sensitivity to prompting and occasional language mixing issues, the model's overall performance and the successful distillation to smaller models demonstrate its potential to advance the field of AI reasoning. The open-source nature of the project and its comprehensive documentation make it a valuable resource for the research community.

3.16 BlackRock Aladdin

3.16.1 Product Overview

BlackRock, a global leader in investment management and technology services, has been at the forefront of integrating AI into financial services. A notable advancement is the introduction of Aladdin Copilot [72], an AI-powered tool designed to enhance the capabilities of BlackRock's flagship investment management platform, Aladdin. Aladdin Copilot aims to make the platform more intelligent and responsive, thereby increasing productivity and providing deeper insights for investment professionals. In addition to Aladdin Copilot, BlackRock has developed eFront Copilot [73], a GenAI tool tailored for its private markets platform, eFront. This tool enables users to access quick analytics and visualize factors such as risk and performance, thereby unlocking new efficiencies and insights in private market investments [72]. These innovations reflect BlackRock's commitment to leveraging AI to transform investment management, providing clients with advanced tools to navigate the complexities of modern financial markets.

**3.16.2 Technology**

BlackRock's integration of GenAI into its investment management platforms, Al-
addin and eFront, is underpinned by advanced technological frameworks designed
to enhance data analysis, user interaction, and decision-making processes.

At the core of Aladdin Copilot and eFront Copilot is the deployment of GenAI
models [72, 73]. These models are trained on extensive datasets pertinent to fi-
nancial markets and investment strategies, enabling them to generate insightful
analyses and responses tailored to complex financial inquiries. The GenAI capabilities facilitate the synthesis of large volumes of data, providing users with nuanced insights that support informed decision-making.

Both Copilot tools employ advanced NLP techniques to interpret and respond to user queries expressed in everyday language. This NLP integration allows users to interact with the platforms through intuitive chatbot interfaces, making complex data retrieval and analysis more accessible. The chatbot interfaces serve as conversational agents, guiding users through data exploration and providing immediate, contextually relevant information.

The Copilot tools function as intelligent agents within the Aladdin and eFront ecosystems. These agents autonomously perform tasks such as data retrieval, analysis, and visualization, reducing manual effort and minimizing the potential for human error. By acting on behalf of the user, these agent-based systems streamline workflows and enhance operational efficiency.

Hosted on Microsoft Azure, Aladdin leverages the cloud platform's robust infrastructure to ensure scalability, security, and high availability [74]. The use of Azure's open APIs facilitates seamless integration of AI functionalities, allowing for rapid deployment and continuous innovation within the platform. This cloud-based approach ensures that the Copilot tools can handle large-scale data processing and deliver real-time insights to users.

BlackRock places a strong emphasis on data privacy and risk management in the deployment of its AI tools. Aladdin Copilot and eFront Copilot are designed with robust content filtering mechanisms and parameters to mitigate risks such as misinformation or inappropriate outputs. Additionally, these tools do not provide investment advice or respond to queries beyond the platform's defined boundaries, ensuring compliance with regulatory standards and maintaining user trust.

### 3.16.3 Functionalities

BlackRock's GenAI tools, Aladdin Copilot and eFront Copilot, offer a range of functionalities designed to enhance investment management processes through advanced data analysis and user-friendly interfaces.

**Aladdin Copilot** Aladdin Copilot functions as an advanced interface within the Aladdin platform, seamlessly integrating GenAI capabilities to address complex investment queries with precision and speed. By providing instantaneous access to relevant data and insights, it empowers users to make critical business decisions confidently. The system's design emphasizes an enhanced user experience, ensuring even novice users can navigate the platform like experts. Furthermore, its configurable features allow users to customize the interface to align with their specific needs, significantly improving operational efficiency. With access permissions
carefully managed, Aladdin Copilot ensures informed decision-making by deliver-
ing tailored analytics and insights that support strategic investment actions.

**eFront Copilot** eFront Copilot revolutionizes how users interact with investment intelligence by enabling natural language query processing. This functionality transforms user prompts into actionable visualizations and sophisticated analytics, bridging the gap between complex data and intuitive understanding. By offering immediate access to key metrics and portfolio trends, it facilitates rapid evaluation of investment performance. The tool also excels in visualizing exposures, performance indicators, and risk factors, presenting users with clear, actionable insights tailored to private market investments. Moreover, its streamlined workflow configuration enables users to optimize layouts and integrate existing Insight functionalities directly through an intuitive chat interface, enhancing overall productivity and operational agility.

Collectively, these functionalities aim to optimize investment workflows, en-
hance data accessibility, and support informed decision-making in both public and
private market investments.

3.16.4 Impact

BlackRock's integration of GenAI through Aladdin Copilot and eFront Copilot has significantly influenced various facets of investment management, leading to notable advancements in efficiency, decision-making, and client engagement.

The deployment of Aladdin Copilot has streamlined data retrieval and analysis processes, enabling investment professionals to access pertinent information swiftly. This immediacy reduces the time spent on manual data gathering and analysis, allowing professionals to focus on strategic decision-making and client interactions.

By providing instantaneous insights and analytics, Aladdin Copilot supports
more informed decision-making. Investment managers can quickly assess risk expo-
sures, performance metrics, and market trends, facilitating timely and data-driven
investment strategies.

The intuitive user experience offered by Aladdin Copilot empowers users to navigate the platform more effectively, enhancing client interactions. The tool's ability to surface actionable information rapidly supports advisors in delivering timely and relevant insights to clients, thereby strengthening client relationships.

eFront Copilot has transformed the management of private market investments by enabling users to generate data visualizations and access analytics through natural language queries. This functionality enhances the understanding of key metrics and trends, leading to more effective portfolio management and investment oversight.
The introduction of these GenAI tools positions BlackRock at the forefront of financial technology innovation. By integrating advanced AI capabilities into their platforms, BlackRock not only enhances its service offerings but also sets a benchmark for the application of AI in investment management.

### 3.16.5 Our Review

Accroding to the former illustration, we find that BlackRock's integration of AI-driven tools like Aladdin Copilot and eFront Copilot represents a critical advancement in financial technology. These tools showcase the company's ambition to redefine investment management processes through the use of GenAI. Their ability to leverage NLP for conversational interfaces and to synthesize vast datasets for actionable insights demonstrates a sophisticated understanding of the needs of modern financial professionals. By lowering the barriers to access advanced analytics and enabling real-time responses to complex queries, BlackRock has significantly enhanced productivity and operational efficiency in the financial services domain.

The adoption of natural language interfaces is particularly commendable, as it simplifies interactions with complex financial data. This innovation broadens the accessibility of BlackRock's platforms to a diverse range of users, from experienced institutional investors to advisors with less technical expertise. Moreover, the integration of GenAI for data synthesis allows for the rapid identification of market trends and portfolio risks, a feature that is invaluable in the context of volatile and time-sensitive financial environments. Similarly, the deployment of eFront Copilot in private markets is a notable achievement, addressing long-standing challenges such as data opacity and illiquidity. By equipping users with visualization tools and analytics for private market investments, BlackRock has effectively expanded the scope of AI applications to areas previously underexplored.

Despite these strengths, there are areas where improvements could significantly enhance the effectiveness and usability of these tools. One key issue is the explainability of the AI models. While the GenAI engines underpinning these platforms excel at generating insights, their decision-making processes often remain opaque. In high-stakes financial contexts, where regulatory compliance and user trust are critical, integrating explainable AI (XAI) frameworks would provide greater transparency and foster confidence among users. Furthermore, while the platforms offer configurable experiences, these configurations may not fully meet the specialized needs of diverse investment strategies. Enhancing the adaptability of the tools by allowing users to define custom prompts, workflows, and analytics would cater to the unique requirements of niche financial practices.

Another potential risk lies in the over-reliance on AI-generated outputs. Although these tools are designed to augment decision-making rather than replace
human expertise, there is a possibility that users may overly depend on AI rec-
ommendations, potentially overlooking nuanced market conditions that require
human judgment. BlackRock could address this by emphasizing the collaborative
nature of AI and human expertise in training materials and user interfaces. This
approach would reinforce the idea that AI is a supportive mechanism designed to
complement, not substitute, professional decision-making.

Looking ahead, the potential for these tools to evolve is immense. Future itera-
tions could incorporate multi-modal AI capabilities, integrating text, visuals, and
even geospatial data to provide a richer analytical experience. Additionally, the in-
tegration of real-time sentiment analysis from news and social media could further
enhance the tools' ability to provide dynamic and context-aware insights. Further-
more, as sustainable finance continues to gain traction, embedding Environmental,
Social, and Governance (ESG) metrics into these platforms would position Black-
Rock as a pioneer in aligning AI-driven tools with the growing demand for ethical
investment practices.

In conclusion, BlackRock's Aladdin Copilot and eFront Copilot exemplify the
transformative potential of GenAI in financial services. While these tools have set
a high standard in terms of efficiency and functionality, further advancements in
explainability, adaptability, and ethical alignment will be crucial to their long-term
success. By addressing these areas, BlackRock can continue to lead the charge in
harnessing AI to shape the future of investment management.

3.17 Citi Assist and Citi Stylus

3.17.1 Product Overview

Citi's latest AI products focus on enhancing internal operations and employee pro-
ductivity. Citi Assist serves as an intelligent information retrieval system, enabling
quick access to internal policies and procedures through natural language queries.
This tool significantly reduces time spent on manual searches while ensuring ac-
curate access to critical organizational data.

Building on this foundation, Citi Stylus brings advanced document processing
capabilities to the workplace [75]. The platform excels in automated document
summarization, comparison analysis, and intelligent search functions. Through its
sophisticated AI algorithms, Stylus helps employees efficiently extract key insights
from extensive documentation, streamlining workflows across HR, risk, compli-
ance, and finance departments.
### 3.17.2 Technology

Citi Bank's generative AI initiatives leverage LLMs to process and analyze vast amounts of internal data [75]. These models understand and respond to natural language queries, providing precise and contextually relevant information. Citi collaborates with Google Cloud to harness its Vertex AI platform, ensuring secure and scalable cloud-native AI solutions. The bank has migrated multiple workloads and applications to Google Cloud, enhancing its data processing and analytics capabilities [76]. Interestingly, Citi Bank does not appear to pretrain its own models; instead, it relies on pre-existing LLM frameworks and cloud-based AI services to power its operations. This approach allows Citi to focus on fine-tuning and optimizing models for specific financial services applications without the overhead of model pretraining.

Citi also employs advanced AI technologies such as optical character recognition (OCR) to automate document processing, improving accuracy and reducing manual effort. Additionally, the integration of AI tools like GitHub Copilot for code development supports Citi's 40,000 developers in modernizing legacy systems and accelerating new application development.

### 3.17.3 Functionalities

Citi Bank's AI initiatives provide a range of functionalities aimed at enhancing employee productivity and customer satisfaction [77]. Citi Assist is an internal AI-powered tool that enables employees to efficiently retrieve information from the bank's policies and procedures. It allows streamlined access to HR, risk, compliance, and finance information, significantly reducing the time spent on manual searches and improving operational efficiency.

Citi Stylus facilitates document summarization, comparison, and search across multiple files, providing employees with concise summaries and comparisons that enhance decision-making processes. This tool enables employees to extract key insights from extensive documentation efficiently, ensuring that critical information is readily available when needed.

Generative AI Developer Tools offer AI-driven code suggestions and auto-completion features that assist developers in writing code more efficiently. These tools help reduce coding errors and support the modernization of Citi's legacy systems, accelerating the development of new applications and improving software quality.

AI-powered document processing automates the extraction and processing of information from various documents using AI and OCR technology. This automation enhances compliance assessments and client onboarding processes by reducing manual errors and speeding up operations, ensuring accuracy and consistency.
Lastly, AI-driven customer service enhancements leverage AI-powered chatbots and virtual assistants to provide personalized interactions. These technologies improve customer response accuracy and timeliness, enhancing client engagement and satisfaction by delivering relevant and context-aware information.

### 3.17.4 Impact

The implementation of generative AI at Citi Bank is projected to yield significant improvements in operational efficiency and client satisfaction[78]. Automated document processing is expected to reduce manual workloads and errors, while AI-driven insights empower employees to make more informed decisions. The adoption of AI-assisted coding tools accelerates software development, helping Citi modernize its technology landscape.

For customers, the use of AI-powered virtual assistants ensures more personalized and timely responses, improving overall experience. Citi's investment in cloud infrastructure and AI technologies positions it as a leader in leveraging generative AI for financial services.

### 3.17.5 Our Review

Citi Bank is leveraging generative AI to transform its internal processes and customer interactions. Through strategic collaborations with Google Cloud and the deployment of LLM-driven solutions, the bank aims to enhance efficiency, accuracy, and customer satisfaction. With a strong focus on document automation, AI-assisted development, and personalized client support, Citi is positioning itself as an innovator in the financial services sector.

## 3.18 Kasisto KAI-GPT

### 3.18.1 Product Overview

Kasisto is a leading provider of conversational and generative AI solutions specifically tailored for the financial services industry [79]. Their suite of products aims to enhance customer satisfaction, improve employee productivity, and drive significant cost savings for financial institutions of all sizes. Key products include KAI Consumer Banking, KAI Answers, KAI-GPT, and the KAI Platform, each designed to address specific needs within the banking ecosystem.

### 3.18.2 Technology

Kasisto's technology is built around KAI-GPT, a large language model specifically designed for the financial industry. This model combines prescriptive and generative
tive AI to deliver accurate, contextually relevant responses. KAI-GPT is trained
on curated financial knowledge sources and millions of banking-specific conversa-
tions, ensuring that responses are both reliable and secure. The platform supports
natural language understanding and is capable of integrating with existing banking
systems through APIs, providing a seamless omnichannel experience.

3.18.3 Functionalities

KAI represents a comprehensive AI ecosystem designed to transform digital inter-
actions across banking and enterprise environments. At its core, KAI Consumer
Banking serves as an intelligent virtual assistant that delivers personalized banking
experiences, capable of handling everything from routine inquiries to sophisticated
financial planning. When complex scenarios arise, the system seamlessly transi-
tions to human support, ensuring comprehensive customer service [80].

Complementing the consumer-facing capabilities, KAI Answers emerges as a powerful knowledge management tool that enables instant, precise information retrieval from diverse data repositories. By facilitating rapid access to critical information, this component significantly enhances employee productivity, reduces onboarding complexities, and promotes consistent process standardization across organizational frameworks [81].

The generative AI capabilities are powered by KAI-GPT, which drives human-
like, personalized interactions across multiple linguistic contexts. This advanced
component can be meticulously customized to embody a financial institution's
unique brand voice and strategic objectives, thereby creating more engaging and
contextually relevant communication experiences [82].

Underpinning these sophisticated functionalities is the KAI Platform, a robust
and flexible infrastructure designed for comprehensive AI solution deployment. Its
multi-tenant architecture allows multiple brands to operate simultaneously, while
providing sophisticated administration tools for content management and sophis-
ticated analytics. This platform serves as the technological backbone that enables
seamless integration and scalable AI-driven interactions across various organiza-
tional channels [83].

3.18.4 Impact

Kasisto's AI solutions have demonstrated significant improvements in operational
efficiency and customer engagement. Financial institutions using KAI have re-
ported reductions in live chat volumes, increased call containment rates, and en-
hanced customer satisfaction. The platform's ability to handle a wide range of
banking-specific queries and provide instant, accurate responses has led to cost
savings and improved employee productivity. Additionally, KAI's multilingual
capabilities and seamless integration with existing systems make it a versatile solution for global financial institutions.

### 3.18.5 Our Review

Kasisto's AI products offer a comprehensive suite of solutions that effectively address the unique challenges faced by the financial industry. The integration of KAI-GPT with existing banking systems provides a powerful tool for enhancing customer interactions and optimizing operational workflows. With its focus on accuracy, transparency, and security, Kasisto's technology stands out as a reliable choice for financial institutions looking to leverage AI to drive growth and improve customer experiences.

## 3.19 Boston Consulting Group Smart Banking AI

### 3.19.1 Product Overview

Boston Consulting Group (BCG) has developed "Smart Banking AI", a comprehensive solution designed to harness the power of GenAI to transform the financial services sector [84]. This product aims to enhance customer engagement, streamline operations, and drive revenue growth for financial institutions.

Smart Banking AI integrates advanced analytics and machine learning models to provide financial institutions with actionable insights. By leveraging GenAI, the platform enables banks to deliver personalized services, optimize internal processes, and improve decision-making capabilities.

BCG's commitment to innovation in the financial sector is further demonstrated through its extensive research and publications on the application of GenAI in financial services. The firm offers strategic guidance to help institutions effectively implement AI-driven solutions, ensuring they remain competitive in a rapidly evolving landscape.

### 3.19.2 Technology

BCG Smart Banking AI leverages a suite of advanced technologies to enhance financial services through GenAI. At its core, the platform employs GenAI to draft hyper-personalized emails, summarize key reports, and produce investment memos, thereby streamlining communication and decision-making processes.

The platform integrates machine learning models to analyze customer data, enabling the identification of high-potential clients and the suggestion of products likely to resonate with individual customers. This predictive capability is crucial for personalized customer engagement and retention strategies.
To ensure flexibility and scalability, Smart Banking AI adopts a modular approach with prebuilt components, including libraries of algorithms, tools, and APIs. This design allows for quick deployment and customization, enabling banks to prioritize specific business lines and functions while seamlessly adding new capabilities as needed.

Furthermore, BCG offers two engagement models for implementing Smart Banking AI: an in-house build supported by BCG for banks capable of managing internal capabilities, and a subscription-based analytics service hosted on BCG's secure cloud platform for those preferring an external solution. This dual approach ensures that a wide range of financial institutions can effectively integrate the platform into their operations.

By combining GenAI with advanced analytics and a flexible, modular architecture, BCG's Smart Banking AI provides financial institutions with a powerful tool to enhance customer engagement, optimize operations, and drive innovation in the financial services sector.

### 3.19.3 Functionalities

BCG's Smart Banking AI offers a comprehensive suite of functionalities designed to enhance various aspects of financial services through the application of GenAI and advanced analytics. The platform employs GenAI to draft hyper-personalized emails, create summaries of key reports, and produce investment memos, enabling financial institutions to deliver tailored content to clients and enhance engagement [84]. Utilizing machine learning models, Smart Banking AI identifies customers' evolving needs and suggests products or services that are likely to resonate with them, supporting proactive engagement strategies and fostering deeper customer relationships. The platform also detects early warning signs of customer churn, such as significant decreases in credit card spending or the initiation of loans with competing banks, allowing financial institutions to engage proactively to retain business. Additionally, Smart Banking AI leverages analytics to assign customers to appropriate review lanes—ranging from in-depth to light-based on their risk profiles, reducing credit review costs by up to 30% while maintaining rigorous risk management standards. Furthermore, the platform provides a centralized dashboard for relationship managers, offering an overview of customer leads and alerts, enabling managers to prioritize actions effectively and monitor performance metrics. By integrating these functionalities, BCG's Smart Banking AI empowers financial institutions to enhance customer engagement, optimize operations, and implement data-driven strategies effectively.
### 3.19.4 Impact

BCG's Smart Banking AI has demonstrated significant impact across various facets of financial services, driving both operational efficiency and enhanced customer engagement. By leveraging GenAI to draft personalized communications and generate insightful reports, financial institutions have achieved a 20% to 30% increase in profits, underscoring the platform's effectiveness in fostering deeper client relationships and identifying revenue opportunities.

Operationally, the platform's predictive analytics and risk monitoring capabilities have led to a 30% reduction in credit review costs, achieved by streamlining processes and accurately assessing customer risk profiles. Additionally, by identifying early signs of customer attrition and enabling proactive engagement strategies, institutions have experienced a 14% decrease in client churn, highlighting the system's role in bolstering customer retention.

The modular design of Smart Banking AI allows for rapid deployment and customization, enabling banks to prioritize specific business lines and functions while seamlessly integrating new capabilities. This flexibility ensures that institutions can adapt the platform to their unique needs, facilitating a tailored approach to digital transformation.

### 3.19.5 Our Review

BCG's Smart Banking AI represents a significant advancement in the integration of GenAI within the financial services sector. Its comprehensive suite of functionalities, including personalized communication, predictive analytics, and risk monitoring, addresses critical needs in modern banking. The platform's modular design and flexibility further enhance its appeal, allowing institutions to tailor the system to their specific requirements.

We can see that the application of GenAI in drafting personalized communications and generating insightful reports is noteworthy. By leveraging large language models, the platform can produce human-like text, enhancing client engagement and operational efficiency. However, it is essential to consider the challenges associated with GenAI, such as the potential for generating plausible but incorrect information, commonly referred to as "hallucinations." Mitigating this risk requires robust validation mechanisms and continuous monitoring to ensure the accuracy and reliability of the generated content.

The platform's predictive analytics capabilities, which identify evolving customer needs and suggest relevant products or services, demonstrate the effective application of machine learning algorithms in financial contexts. These models analyze vast datasets to uncover patterns and trends, enabling proactive engagement strategies. Nonetheless, the success of these predictive models heavily depends
on the quality and diversity of the input data. Biases in the data can lead to skewed predictions, potentially affecting customer satisfaction and trust. Therefore, implementing rigorous data governance frameworks is crucial to maintain the integrity of the analytics.

In terms of risk monitoring, Smart Banking AI's use of analytics to assign customers to appropriate review lanes based on their risk profiles is a practical approach to resource optimization. By focusing attention where it is most needed, institutions can reduce costs while maintaining robust risk management practices. However, it is important to ensure that the risk assessment models are transparent and interpretable, allowing for human oversight and intervention when necessary.

The platform's modular architecture, featuring prebuilt components such as libraries of algorithms, tools, and APIs, offers significant advantages in terms of scalability and customization. This design enables financial institutions to prioritize specific business lines and functions, adding new capabilities as needed. However, the integration of these modules into existing systems may present challenges, particularly concerning interoperability and data consistency. Careful planning and execution are required to ensure seamless integration and to realize the full benefits of the platform.

In conclusion, BCG's Smart Banking AI leverages advanced GenAI and machine learning technologies to offer a robust solution for modern financial institutions. While the platform presents numerous benefits, careful consideration of potential challenges, including data quality, model interpretability, and integration complexities, is essential. By addressing these factors, institutions can effectively harness the power of GenAI to drive innovation and efficiency in the financial services sector.

## 3.20 RBC Borealis ATOM

### 3.20.1 Product Overview

ATOM (Asynchronous Temporal Model) is RBC Borealis' foundation model for financial services, developed by one of the largest AI research teams in banking [85]. Unlike traditional language models, ATOM specializes in processing asynchronous event sequences like transaction data, stock trades, and client interactions. The platform represents RBC's strategic approach to building customized AI models specifically for banking, developed over the past two years to process the bank's entire transactional history.
### 3.20.2 Technology

ATOM is built as a Large Transaction Model (LTM), analogous to Large Language Models but specialized for financial data [86]. The technology runs on RBC's proprietary infrastructure, which includes one of Canada's largest enterprise-focused GPU clusters built in partnership with NVIDIA. Unlike cloud-based solutions, ATOM operates entirely on RBC's internal infrastructure [87], ensuring data security and regulatory compliance. The model employs advanced temporal point processing techniques and specialized neural network architectures designed for asynchronous data patterns.

### 3.20.3 Functionalities

The platform provides comprehensive financial analysis capabilities including credit assessment, risk evaluation, and behavioral pattern recognition. Through NOMI [88], RBC's personal banking product, ATOM powers features like bill payee recommendations, cash flow forecasting, and bill-reversal detection. The model can analyze complex patterns in transaction sequences, from payroll deposits to subscription payments, creating detailed representations of client transacting behavior. Its ability to understand modern financial complexities extends beyond traditional salary-based assessments to accommodate gig economy income patterns.

### 3.20.4 Performance & Impact

ATOM has demonstrated significant impact in practical banking applications, particularly in credit assessment where it has enabled lending to previously ineligible clients through more sophisticated risk analysis. The model's success has led to its integration with RBC's NOMI service, which has received industry recognition. RBC has invested heavily in the platform's development, with a team of approximately 40 researchers contributing to over 50 published papers in major AI conferences and journals [86].

### 3.20.5 Our Review

ATOM represents a significant innovation in financial AI, differentiating itself through its focus on temporal data patterns rather than traditional language processing. The platform's emphasis on internal infrastructure and domain-specific modeling shows RBC's commitment to developing secure, specialized AI solutions for banking. While currently focused on internal applications, the planned integration with Cohere's language models suggests potential for broader applications. The platform's success in balancing innovation with regulatory compliance and risk management makes it a notable example of practical AI deployment in banking.
## 3.21 Visa Account Attack Intelligence

### 3.21.1 Product Overview

The Visa Account Attack Intelligence (VAAI) Score is a cutting-edge fraud prevention solution powered by generative AI [89]. The tool is designed to combat account enumeration attacks, a common tactic used in card-not-present (CNP) transactions where fraudsters utilize automated scripts and botnets to test and validate payment card details. These attacks can cause significant financial losses and operational disruptions.

VAAI analyzes transaction patterns in real time and assigns a risk score to each transaction. This risk score enables issuers to quickly detect and prevent fraudulent activities while minimizing disruptions to legitimate transactions. By leveraging generative AI, the tool significantly reduces false positives—by up to 85% compared to traditional models—ensuring higher accuracy and better customer experience.

Currently, VAAI is available to US-based issuers with plans to expand its reach worldwide. The solution aims to enhance fraud detection capabilities, reduce operational costs associated with fraud management, and strengthen overall payment security in the digital age [90].

### 3.21.2 Technology

VAAI Score leverages advanced generative AI to enhance fraud detection and prevention in CNP transactions. At its core, VAAI combines sophisticated machine learning algorithms and real-time data analysis to effectively address enumeration attacks.

VAAI uses generative AI to identify and learn transaction patterns indicative of enumeration attacks. This technology enables the system to differentiate between legitimate and suspicious activities, even in large volumes of transactions. VAAI assigns a dynamic risk score to each transaction based on the likelihood of fraudulent behavior. This real-time evaluation allows issuers to act promptly, reducing potential losses. Using AI-driven insights, VAAI minimizes false positives by up to 85%, ensuring that legitimate transactions are not flagged unnecessarily, thus improving the user experience for cardholders. VAAI is designed to process vast amounts of transaction data, making it highly scalable for financial institutions of all sizes. Technology continually evolves to adapt to emerging fraud tactics, ensuring long-term efficacy.

Technological innovations make VAAI a powerful tool in combating account enumeration and protecting both issuers and cardholders from fraud in an increasingly digital payment ecosystem.
### 3.21.3 Functionalities

VAAI Score offers a range of functionalities designed to prevent and mitigate fraud in CNP transactions. At its core, VAAI provides a real-time transaction risk assessment by assigning a dynamic score that reflects the likelihood of fraudulent activity. This enables issuers to take immediate and informed actions, such as blocking suspicious transactions or prompting additional verification. VAAI also excels in fraud pattern detection, leveraging generative AI to identify behaviors indicative of enumeration attacks, such as rapid-fire testing of card details. Furthermore, the system significantly reduces operational disruptions by minimizing false positives, ensuring that legitimate transactions are processed seamlessly. With its adaptive learning capabilities, VAAI continuously refines its algorithms to counter evolving fraud tactics, making it a robust and future-ready solution for issuers to combat account attacks effectively.

### 3.21.4 Impact

By allowing issuers to identify and prevent enumeration attacks in real time, VAAI helps mitigate financial losses and operational burdens associated with fraudulent activities. Its advanced generative AI reduces false positives, improving the accuracy of fraud detection and enhancing the customer experience by minimizing unnecessary transaction declines. The system also reduces the workload on fraud management teams by streamlining the identification of high-risk transactions. Beyond immediate benefits, VAAI strengthens the overall security of the payment ecosystem, fostering greater trust between issuers, merchants, and consumers. As fraud tactics evolve, VAAI's adaptive learning capabilities ensure that it remains a critical tool for maintaining secure and reliable digital payment systems.

### 3.21.5 Our Review

VAAI Score is a major advancement in fraud prevention for CNP transactions. By leveraging generative AI, it effectively detects enumeration attacks, provides real-time risk scores, and reduces false positives, ensuring a seamless user experience. Its scalability and adaptability make it future-proof, though its current availability is limited to U.S. issuers, highlighting the need for global expansion. Overall, VAAI combines innovation and practicality to enhance digital payment security and trust.
# 4 Conclusion

The adoption of Gen AI in the financial sector signifies a transformative shift in leveraging artificial intelligence to reshape operational efficiency, decision-making, and customer experiences. Current implementations, ranging from automated advisory systems to advanced fraud detection tools, showcase the profound impact of LLMs and other technologies in delivering personalized services and optimizing financial workflows. Yet, their integration requires a careful balance of innovation with considerations for ethical implications, data privacy, and regulatory compliance.

As the financial sector continues to evolve, the opportunities for advancing Gen AI applications remain vast. Efforts to enhance domain-specific customization, integrate multimodal capabilities, and ensure robust security measures will be pivotal in addressing existing challenges. Collaboration among financial institutions, AI developers, and regulatory bodies will be essential to fully realize the potential of these technologies.

The journey of integrating Gen AI into finance is far from complete. The ongoing advancements in AI, coupled with a focus on accessibility and scalability, promise a future where financial institutions can deliver innovative, efficient, and inclusive services. By navigating the complexities of ethical use and maintaining transparency, the financial sector can ensure that LLMs not only drive competitiveness but also meet the evolving needs of their customers, setting a benchmark for the responsible adoption of AI-driven solutions.

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