WHITE PAPER

The signal-oriented
bank of the future

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THE SIGNAL-ORIENTED BANK OF THE FUTURE#1
The power of deep learning
to drive customer engagement
Over the past twenty years, organizations have applied big dataand quantitative analytics to the decision-making process acrossthe enterprise. In other words, they have become data-drivenorganizations.
Data-driven organizations have proven to be the most successful
at delivering innovative product design, agile business processes,
personalized direct marketing, and the digital transformation of
their customer-facing processes.
Over the next ten years, successful banking
organizations will evolve from being data-driven to
becoming signal-oriented. This paper will look at thefunctionality that deep learning and foundation
models will soon be capable of offering to banking
and financial institutions and how this potential can betapped to reimagine the customer experience.
Deep learning and foundation models will provide banking
organizations with the capability to make human-like decisions,allowing them to evolve beyond workflow automation toworkflow independence.
Deep learning and foundation models will be able to provide
actionable insights - in the banking industry often referred to assignals, derived from structured and unstructured data to informbusiness processes and drive positive outcomes. For example, asignal of “churn risk” is generated when a customer with a highbaseline risk of churn says “I want to close my account” to a callcenter agent after discovering a fraudulent payment.

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Identification of the signal via deep learning models
Deep learning and foundation models power applications like Natural Language Processing(Google translate), User Content Recommendations (Netflix) and Computer Vision (Facebookphoto facial recognition). Deep learning and foundation models are the state-of-the-art for
artificial intelligence; it’s the “tip of the spear”.
Organizations will become “signal oriented” by enabling deep learning and foundation models
at scale in order to better understand their customers, predict their customers’ needs, andoptimize the value they provide to their customers – all with results which are directly measurable.
A great illustration of the potential of deep learning andfoundation models to transform business processes is in thecustomer-facing call center. Customer-originated speech
and text data is the most underleveraged information asset in
the enterprise, yet it has the greatest potential to provide
insights as it is the one time that customers share, in their own
words, what they want and need.

Enterprise-Wide
Speech & Text Corpus

ChatVoice

Text

Transcripts

Documents

EmailsSocialSambaNova Dataflow-as-a-Service TM
Foundation Model

Signal: Downstream
Language Tasks

Sentiment
Analysis

Named Enitity
Recognition

Form
Understanding

Document
Classification

Content
Summarization

Multi-Language
Translation“Language as-a-Service” Signals

Risk & Compliance

Universal Bank
Application Ecosystem

Consumer &Community Banking

Corporate &Investment Banking

CommercialBanking

Asset & WealthManagementFraud`3

THE SIGNAL-ORIENTED BANK OF THE FUTURE

Speech and text are examples of unstructured data, meaningthat the data does not fit into a tabular row/column format andis unlabeled, both of which are necessary conditions forsupervised learning modeling techniques. As a result,organizations have been relegated to using traditional text
analysis techniques such as Naïve Bayes which use characterand string-based approaches to identify dataset features suchas document classification, sentiment analysis or named entity
extraction.
With the emergence of deep learning and foundation models,a radically innovative approach to speech and text analysis will
become possible. Organizations will soon be able to unlock
meaningful insights of customer intent and propagate thesesignals across their application ecosystem to drive in-themoment customer interactions facilitating the strategic shift fromtraditional to digital channel delivery.
When applied to unstructured data, deep learning andfoundation models will essentially create an understanding oflanguage structure which presses the point that organizations
are currently approaching language tasks in the wrong order –their AI systems should first understand language structure before
defining tasks. A contextual understanding of language provides
a richer representation of the communication and can produce
a more salient signal for distribution as a business service via
enterprise application workflows.

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The evolution of deep learning: foundation models
Foundation models are very powerful deep learning models thatare defined by their massive scale which enables them to unlock
insights trapped in unstructured data, in-context learning throughpowerful emergent capabilities with zero and few shot prompts,
and high versatility with the ability to solve dozens of different
tasks with a single model. In the natural language domain, GPT-3
is a Foundation Model with 175 billion parameters and has thepotential to perform tasks such as document summarization,
document classification, named entity extraction and sentiment
analysis with a single model.
Foundation model outputs have the potential tobecome inputs to upstream business models. Thisapproach will significantly collapse a portion of thethousands of models spread across the enterprise
down to a handful of foundation models, eliminating
considerable hidden costs in the maintenance of themodel catalog while establishing a model
development process with more agility and control.
Foundation models will play a pivotal role in organizational data
management strategies. The foundation models will be able toassist with defining the value of the data and the potentialchange in value over time based on the access patterns of thedata asset in the model pipeline. The output signal can thenbecome further enriched and emergent in its capabilities as its
value is realized via the distribution of the signal across theorganization’s application workflows. This reduction in data andmodel redundancy will be the key to environmental sustainability– a topic which is a board-level discussion.

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THE SIGNAL-ORIENTED BANK OF THE FUTURE#4
Propagation of signals throughout the enterprise
Value can be derived when an output signal from a Foundation
Model is turned into a business service that allows consumptionof that signal by enterprise business workflows.
At SambaNova, we understand that for customers,
value is not achieved by deploying a model as a point
solution but rather from the number of times thatsignals are persisted and consumed acrossapplication workflows. In this illustration, we are taking
the position that all data, both structured andunstructured, will flow through the SambaNova
platform to deliver value.
Data will be able to stream into the foundation models
which then emit output signals that can be utilized as abusiness service and seamlessly integrated within thebusiness workflows across the vertical and horizontal
application ecosystem of the enterprise; in thisexample, “Next Best Action”.
Organizational obsession with customer-centricity as a strategic
imperative will be materialized via the hyper-personalization ofcustomer interactions. This will allow the acceleration of the
migration of routine servicing activity to digital channels. Any
investment in personalization that does not include speech andtext is undermining the strategic initiative by not addressing theobvious statement that customer service begins with anunderstanding of the customer task requiring resolution and thenecessity to draw upon customer verbatims to focus on
customer task.
At SambaNova, we believe that the bank of the future will need
to be able to ingest all customer-generated data, including
unstructured data such as speech and text. To achieve this, they
will need a platform that is capable of evaluating these inputs
and producing signals to inform the “Next Best Action” which willthen be spread across the bank’s enterprise workflows including
servicing channels, payments platforms, fraud mitigation
processes, and direct marketing tools.

6THE SIGNAL-ORIENTED BANK OF THE FUTUREEmail

Data Flow:
SourcesEnterprise Systemsof Record

ChannelBehaviors

ChatExternal
SourcesDocuments
Contact Center

Customer Profile

Data Flow:
Multi-Structured
Datasets

Machine3rd Party
Speech, Text,Videos, Images

Products
Transactions

SambaNova Dataflow-as-a-Service TM
Foundation Model

Signal:Democratization

Singal:Next Best
Action Workflow

Signal Customer Intent

Customer Identity Verification

Share of Total Financial Wallet

Payment Instrument Preferences

Customer Task Resolution Status

Product Cross-Sell Propensity

Share of Total Spending

Degree of Digital Channel Migration

Sales Channel Preference

Likelihood of Deposit Balance BulidRisk of Customer Attrition
Level of Customer Satisfaction

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Evaluation of the impact on the customer
Banking organizations will be able to identify the sequence ofactivities within the contact center workflow for resolving thecustomer’s task within their banking journey. This will include thetechnology components of the contact center ecosystem, theclient and banker interaction points within the workflow, the flowof data available to resolve the customer task at each step inthe workflow, and the associated business metrics captured tomeasure the bank's workflows efficiency and effectiveness.
Given the data available, the ability to produce signals canpositively impact the core business metrics. By linking the metricimpact on a step in the process, banking organizations will beable to further link to its impact on customer experience. The
customer experience links to customer retention, and retention
to the critical bank outcomes of revenue preservation and costsavings relative to the number of deposit clients of the bank.
The banks of the future will need a platform that will sit
upstream and the input data, regardless of structure,
will be threaded into a set of foundation models
running in parallel to radically accelerate thethroughput, velocity, and salience of signals as
outputs, which are disseminated across the various
application services at enterprise scale.
This platform will need to integrate with the client’s data andanalytics supply chain to represent the flow of data and how itwill be created, captured, organized, stored, and accessed foranalytics. Additionally, this platform must not disrupt, but rather
simplify, the process of deploying AI by seamlessly integrating
deep learning and foundation models into the supply chain
and microservices design pattern for application services.
As an example, organizations will be able to highlight speech
and text that is processed and persisted as a language signal
to an enterprise analytical CRM application to be picked up
and leveraged within the existing arbitration logic for Next Best
Action workflows; the same signal (or derivative thereof) canpotentially be leveraged within the risk decisioning process ofthe new product application.

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THE SIGNAL ORIENTED BANK OF THE FUTURE#6IN SUMMARY: The impact on customer engagement
Organizations are struggling to deploy the deep learning andfoundation models necessary to exploit their extensive
investment in harvesting data – especially customer-generated
speech, text, and other unstructured data – and to use
predictive models to drive customer engagement. To achievethis, banking organizations will require a platform that delivers
foundation models that will be capable of persisting signals ofcustomer intent throughout their business workflows, eventually
allowing these signals to drive critical-path business outcomes via
a deep understanding of their clients.

9Next steps
If you would like to learn more
about how banking organizations
are preparing for the future,
visit SambaNova.ai

Learn more at SambaNova.ai
linkedin.com/company/sambanova@SambaNovaAI
info@sambanova.ai

Customers turn to SambaNova to quickly deploy state-of-the-art AI capabilities to meet the demands of the AI-enabled world. Our
purpose-built enterprise-scale AI platform is the technology backbone for the next generation of AI computing. We enable
customers to unlock the valuable business insights trapped in their data. Our flagship offering, Dataflow-as-a-ServiceTM, overcomesthe limitations of legacy technology to power the large complex foundation models that enable customers to discover new services
and revenue streams, and boost operational efficiency. Headquartered in Palo Alto, California, SambaNova Systems wa s foundedin 2017 by industry luminaries, and hardware and software design experts from Sun/Oracle and Stanford University. Investors include
SoftBank Vision Fund 2, funds and accounts managed by BlackRock, Intel Capital, GV, Walden International, Temasek, GIC, Redline
Capital, Atlantic Bridge Ventures, Celesta, and several others.
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