SWIFT INSTITUTE
WORKING PAPER
FAIRNESS AI – FINFAIR AI INDEX FRAMEWORKBADR BOUSSABATPUBLICATION DATE: JUNE, 2024

The views and opinions expressed in this paper are those of the authors. SWIFT and the SWIFT Institutehave not made any editorial review of this paper, therefore the views and opinions do not necessarilyreflect those of either SWIFT or the SWIFT Institute.
I would like to express my sincere gratitude to Dr. Jassim Haji and Dr. Patrice Latinne for their invaluableguidance and advice in the preparation of this paper.

1Table of content:
Abstract
1.Introduction
2.Literature review
The Challenge of Integrating Fairness in Predictive Models ......................................................................... 4
Purpose and Scope of the Research ............................................................................................................. 5
Purpose ..................................................................................................................................................... 5
Scope ......................................................................................................................................................... 5
Fairness through science............................................................................................................................... 6
3.Fairness: definition
Sensu lato definition ................................................................................................................................... 13
Sensu stricto definition ............................................................................................................................... 13
Fair process ................................................................................................................................................. 14
Data bias ..................................................................................................................................................... 15
Origin of data bias ................................................................................................................................... 15
Manifestation of data bias ...................................................................................................................... 15
Impact of data bias ................................................................................................................................. 15
Model Bias .................................................................................................................................................. 15
Origin of model bias ................................................................................................................................ 16
Manifestation of model bias ................................................................................................................... 16
Impact of model bias .............................................................................................................................. 16
4. FinFair AI Index framework: methodology
Description .................................................................................................................................................. 18
Organic Solidarity vs Mechanic Solidarity ................................................................................................... 19
Structural functionalist paradigm ........................................................................................................... 20
Fairness scoring model development ..................................................................................................... 21
From mechanical to organic solidarity in finance ................................................................................... 21
Collaboration between public authorities and financial players ............................................................ 21
FinFair AI Index framework ..................................................................................................................... 22
0. Final equation: ................................................................................................................................ 23
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1. Fair process: variables ..................................................................................................................... 23
2. Data bias: variables ......................................................................................................................... 25
3. Model bias: variables ...................................................................................................................... 28
4. Model bias: Groups ......................................................................................................................... 32
5. Data bias vs Model bias ...................................................................................................................... 35
Weighting, justification and thresholds .................................................................................................. 36
5. Debate
Organic Solidarity towards de facto Fairness ............................................................................................. 41
Operational risks for the FinFair AI Index Framework ................................................................................ 42
Fairness vs Performance ............................................................................................................................. 43
6. Conclusion
7. References

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Abstract
In the universe of financial analytics, particularly business-to-business (B2B) models that
employ AI, fairness is of utmost importance. This study investigates the aspects of fairness
within B2B financial transactions and addresses a significant gap in the existing literature. It
presents a comprehensive framework for assessing fairness in B2B financial models,emphasizing the authenticity and reliability of the data inputs. This study introduced an
innovative approach and proposed a pioneering method for fairness scoring withcustomizable parameters. This study offers a dual contribution to the field of AI fairness,enriching theoretical understanding and providing practical insights for financial
institutions. This study underscores the need for a broader perspective on AI fairness,
extending beyond regulatory compliance to embrace holistic fairness in finance. This
research direction will guide future inquiries into AI practices in the B2B financial domainthat are ethically grounded and attuned to the nuances of fairness.

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1. Introduction
This study is situated within the evolving landscape of financial systems, specifically focusing
on the transition from mechanical to organic solidarity in finance, as framed by the structuralfunctionalist paradigm. This shift is particularly relevant in the context of AI-driven B2B
finance, where traditional practices are reshaped by technological advancements and ethicalconsiderations. The core of this study revolves around the development of a fairness-scoringmodel designed to navigate and enhance fairness in financial decision-making.
This study unveils the FinFair AI Index framework, a comprehensive tool that encapsulates
critical elements of fairness in AI applications within the financial sector. This framework
integrates the variables pertaining to a fair process and model/data bias. The introductionof this framework represents a significant shift in financial models, moving from a uniform,
one-size-fits-all approach (mechanical solidarity) to a more nuanced and adaptable strategy(organic solidarity). This transition is not only a response to technological evolution but also
a proactive effort to embed ethical considerations into AI practices in finance.
This study emphasizes the importance of collaboration between public authorities andfinancial players in this endeavor. By fostering a collaborative environment, the study seeks
to ensure that the financial sector can effectively adapt to the challenges and opportunities
presented by AI and machine learning, thereby contributing to AI fairness in the B2B
financial system.

The Challenge of Integrating Fairness in Predictive Models
The integration of fairness into predictive models in the B2B financial sector presents acomplex and multi-faceted challenge. This complexity stems not only from the technicalaspects of AI, but also from the nature of decision-making processes. In this section, we
explore the hurdles and considerations that complicate the pursuit of fairness in predictivemodeling in the B2B financial context.
One primary challenge lies in defining fairness. While conceptually universal, fairness is aconstruct that varies significantly in interpretation and implementation across different
domains and applications. In predictive models, fairness must balance accuracy with ethicalconsiderations to ensure that decisions are made without undue bias or discrimination
against groups or entities. However, what constitutes bias in a B2B financial model can besubstantially different from consumer-facing models, which often require a deeper analysis
of industry-specific dynamics and stakeholder relationships.
Another significant hurdle is the inherent limitations and biases present in the data fed into
these models. Predictive models are only unbiased as they process data. In many cases,
historical data in the financial sector may reflect existing prejudices or skewed
representations, leading to models that perpetuate these biases inadvertently. Identifying,5

quantifying, and correcting such biases pose substantial challenges, especially in complex
financial environments where data sources are diverse and multi-layered.
Moreover, the technical design and implementation of predictive models introduces
additional layers of complexity. The choice of algorithms, weighting of inputs, andinterpretation of outputs influence the fairness of the model. Balancing the technicaldemands of accuracy and reliability with the ethical imperative of fairness requires not only
advanced analytical capabilities, but also a profound understanding of the ethicalimplications of AI in finance.

Purpose and Scope of the Research
PurposeThe main objective of this study is to create an extensive and progressive scoring framework
for assessing the fairness of AI systems in the business-to-business (B2B) financial sector.
This framework, based on a multifaceted approach to fairness, aims to instigate atransformative change in the financial industry towards the realization of fair outcomes. The
intention was to enhance the present structure of the financial sector and promote fairer
applications of AI.

ScopeThis study encompassed several key dimensions.
Definition of fairness: The definition of fairness will be the starting foundation of
the development of our framework▫ Presentation of the new framework: We present a new paradigm based on
structural functionalism to develop our FinFair AI Index for AI models.
▫ FinFair AI Index Development: This study introduces a mathematical equation. This
serves as the foundation of the scoring model and quantifies the fairness of the AI
models utilized by the financial sector.
▫ Debate on the framework: Implementation of the framework in the financial sector
presents several challenges that must be addressed.
▫

62. Literature review
In this comprehensive literature review, we explore the development of fairness as a concept
in AI with a specific focus on its implications within the B2B financial sector. This inquiry isorganized to systematically analyze seminal works in the field, thereby elucidating theevolution of the understanding of fairness in financial AI. Our objective is to present a
cohesive narrative, tracing the contributions of each research study to a broader
comprehension of fairness, its theoretical foundations, and its practical applications in AI
systems within finance. Our aim is to synthesize these works into a coherent overview,emphasizing significant developments and shifts in thought within this critical domain of
study. By scrutinizing the trajectory and intricacies of these scholarly contributions, thisstudy aims to establish a comprehensive and well-informed understanding of fairness infinancial AI, which is crucial for our approach in this research.
This review provides a starting point for our exploration of fairness in the financial sector,
particularly in B2B settings. Building upon and expanding upon the foundational works
discussed herein, we seek to offer new perspectives and innovative approaches to financial
AI fairness. Our ultimate goal is to contribute significantly to the ongoing discourse on
fairness in the financial industry, ensuring that our research has a meaningful impact on bothacademic and practical realms.

Fairness through science
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R. (2012). Fairness throughawareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference,
214-226. https://doi.org/10.1145/2090236.2090255
Approach to Fairness: Dwork et al. present a pioneering framework in the realm ofalgorithmic fairness, with a focus on the concept of "individual fairness." Thisapproach is groundbreaking in its emphasis on treating similar individuals, aprinciple that can be particularly transformative in the business-to-business financial
sector.
▫ Definition of Fairness: Fairness is defined in this study as a metric-based approach,wherein the level of fairness is assessed by measuring the distance between
individuals in a metric space. In the context of the B2B financial sector, companies
that are close in terms of financial well-being, risk profiles, and other pertinent
metrics ought to be treated equitably by AI algorithms.
▫ Proposed Solutions: The authors present a theoretical framework in which thefairness of an algorithm is determined by its ability to produce comparable results forcomparable individuals. In the context of business-to-business finance, this entails
the development of artificial intelligence models that consistently and equitably
evaluate businesses based on their financial data. This paper also emphasizes thesignificance of carefully defining the metric space in which similarity is assessed, as▫

7this is essential for ensuring that the AI's assessment of similarity aligns with theprinciples of fairness.
Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review,
104(3),
671-732.
Retrievedfrom
http://www.californialawreview.org/wpcontent/uploads/2016/06/5-Big-Datas-Disparate-Impact.pdf
Approach to Fairness: Barocas and Selbst’s seminal work provides a deep
exploration of the intricacies associated with big data and its ramifications forfairness, with a particular emphasis on artificial intelligence systems. Theirinvestigation centers on the idea of disparate impact, a form of unintentional
discrimination that can emerge even in the absence of malevolent intent, which holds
relevance within the financial services sector.
▫ Definition of Fairness: This study delineates disparate impact as a situation whereinalgorithms, despite ostensibly being impartial, yield outcomes that unfairly impair
specific demographics. In the context of B2B financial services, this could occur when
AI systems unintentionally demonstrate preferences or impose penalties on welltargeted businesses based on their size, location, or market sector as a result of biased
data or inadequate model assumptions.
▫ Proposed Solutions: Barocas and Selbst underscored the significance of vigilanceand proactive steps in mitigating disparate impacts. They advocate extensive testing
and auditing of AI systems to detect and rectify any potential biases. Moreover, thepaper recommends enhancing transparency in AI decision-making processes andimplementing fairness-conscious modeling techniques. These approaches areindispensable for financial institutions leveraging AI, as they guarantee that thesystems are not only productive but also just and devoid of unintentional prejudice.
▫

Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning.
Advances in Neural Information Processing Systems, 29, 3315-3323. Retrieved fromhttps://papers.nips.cc/paper/6374-equality-of-opportunity-in-supervised-learning.pdf
Approach to Fairness: Hardt et al. propose a novel approach to fairness in AI,
centered around the concept of "equality of opportunity." This approach isparticularly relevant in supervised learning contexts, in which predictive models playa crucial role in decision-making processes, such as in the B2B financial sector.
▫ Definition of Fairness: The present document delineates the concept of fairness asa state in which individuals who exhibit similarities with respect to a specific task areawarded comparable predictions. In the context of business-to-business financial
dealings, this notion guarantees that corporations with similar financial health andrisk profiles are impartially evaluated by AI systems.
▫ Proposed Solutions: To attain fairness, Hardt et al. incorporated a fairness
constraint into the model training process. The central concept entails equalizing thetrue positive rates among diverse groups, thereby guaranteeing AI's impartial andconsistent performance across various segments of the financial market. This▫

8

approach represents a pivotal advancement by proposing a tangible and quantifiable
method for integrating fairness into AI algorithms, particularly for developing faircredit scoring and risk assessment models in finance.
Zafar, M. B., Valera, I., Rodriguez, M., Gummadi, K. P. (2017). Fairness beyond disparate
treatment & disparate impact: Learning classification without disparate mistreatment.
Proceedings of the 26th International Conference on World Wide Web, 1171-1180.
https://doi.org/10.1145/3038912.3052660
Approach to Fairness: Zafar et al. delved into the subject of fairness in artificial
intelligence by focusing on the issue of disparate mistreatment. Their work was set
against the backdrop of classification problems, which are particularly relevant infinancial decision-making systems.
▫ Definition of Fairness: The study’s primary focus lies in the concept of fairness,
which entails the prevention of disparate mistreatment arising from differential
accuracy rates among various groups. In the context of B2B financial transactions,this principle demands that AI models achieve consistent predictive accuracy acrossa wide range of businesses regardless of their size, sector, or other distinguishing
characteristics.
▫ Proposed Solutions: Zafar et al. presented a methodology that aims to alleviate
disparate mistreatment by integrating fairness constraints into the classifier
optimization problem. They proposed a convex proxy for fairness that enabled theprovision of efficient and scalable solutions. This approach is essential for financial
institutions that strive to develop artificial intelligence systems that are both highly
accurate and fair for treating various business clients.
▫

Kleinberg, J., Mullainathan, S., Raghavan, M. (2017). Inherent trade-offs in the fair
determination of risk scores. Proceedings of the 8th Innovations in Theoretical Computer
Science Conference. https://doi.org/10.4230/LIPIcs.ITCS.2017.43
Approach to Fairness: The study conducted by Kleinberg et al. focuses on a crucial
issue in the realm of AI in finance: the presence of inherent trade-offs when striving
for fairness in risk score determination. Their research is of paramount importancein comprehending the intricate dynamics involved in the development of fair AI
systems for financial decision making, particularly in the B2B sector.
▫ Definition of Fairness: The present study delves into the issue of fairness in therealm of risk assessments, where the aim is to guarantee that artificial intelligencesystems do not perpetuate or intensify existing prejudices against particulardemographics. The authors examined the notion that diverse notions of fairness may
come into conflict with one another, which presents a challenge in developing an AI
system that can meet all fairness criteria concurrently.
▫ Proposed Solutions: Kleinberg and his associates have examined the mathematicalfoundations of these trade-offs, which are achieved through the application oftheoretical models. They argue that the simultaneous attainment of all types offairness may be an unrealistic goal, and thus, a crucial understanding of these trade▫

9offs is necessary in making informed decisions regarding which fairness criteria toprioritize. This insight is particularly pertinent for financial institutions operating in
the B2B sector, where risk assessment models must balance accuracy with fairness
towards diverse client groups.
Hildebrandt, M. (2018). Privacy as protection of the incomputable self: From agnostic toagonistic machine learning. Theoretical Inquiries in Law, 20(1), 83-121.
https://doi.org/10.1515/til-2019-0004
Approach to Fairness: Hildebrandt's work offers a distinctive perspective on
fairness in AI, particularly in the finance sector, by concentrating on the nexus ofprivacy, personal data, and machine learning. This approach is essential forcomprehending the far-reaching consequences of AI in B2B financial settings where
personal and business data are frequently intricately entwined.
▫ Definition of Fairness: Hildebrandt's analysis of fairness is informed by her
consideration of privacy and the incalculable elements of individuals andorganizations. She maintained that achieving genuine fairness in AI systems,
especially in critical domains such as finance, necessitates considering the intricate
and multifaceted nature of personal and commercial identities that cannot be fully
captured through quantification and computational processes.
▫ Proposed Solutions: The paper proposes a transition from "agnostic" to "agonistic"machine learning, emphasizing the importance of respecting the incomputableaspects of identity and avoiding oversimplification or misinterpretation of thesecomplexities. In the realm of B2B finance, this entails creating AI models that are
cognizant of the subtleties of business operations and contexts and that safeguard theprivacy and intricacy of business data. Hildebrandt underlines the necessity of AI
systems that are transparent, accountable, and developed with an understanding of
the ethical ramifications of their incorporation in decision-making procedures.
▫

Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P.,
Martino, J., Mehta, S., Mojsilovic, A., Nagar, S., Ramamurthy, K. N., Richards, J., Saha, D.,
Sattigeri, P., Singh, M., Varshney, K. R., & Zhang, Y. (2018). AI Fairness 360: An extensible
toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv
preprint arXiv:1810.01943. Retrieved from https://arxiv.org/abs/1810.01943
Approach to Fairness: Bellamy et al. introduced the AI Fairness 360 toolkit, which
is a comprehensive suite of metrics and algorithms designed to detect, understand,and mitigate unwanted biases in AI systems. This toolkit represents a significant
advancement in the field of AI fairness, particularly for B2B financial services, inwhich decisions made by AI can have substantial implications.
▫ Definition of Fairness: The AI Fairness 360 toolkit is constructed on the foundationthat fairness in artificial intelligence is multifaceted and demands a diverse array of
metrics and methods to evaluate and guarantee. This toolkit presents acomprehensive collection of fairness measurements and algorithms, acknowledging▫

10

that fairness is not a uniform concept, particularly in intricate and diverse B2B
financial settings.
▫ Proposed Solutions: This toolkit presents a comprehensive selection of algorithms
designed to reduce bias in datasets and models, making it an indispensable asset for
financial institutions committed to guaranteeing the fairness of their AI systems. It
empowers users to select from a diverse array of fairness definitions and applies themost relevant algorithms to their unique situations. Adaptability is essential in theB2B financial industry, where AI applications encompass credit scoring, risk
management, and personalized financial advice.
Mehrabi, N. et al. (2019) - "A Survey of Bias and Fairness in Machine Learning"
Approach to Fairness: Mehrabi et al. offer a comprehensive examination of fairness
in machine learning, emphasizing its multifaceted character. They delineate thediverse biases and fairness definitions that have arisen in AI research, rendering theirstudy an indispensable reference for comprehending the intricacies of fairness.
▫ Definition of Fairness: The study delineates various facets of fairness, including, but
not limited to, demographic parity, equality of opportunity, and individual fairness.
The former pertains to the achievement of fair results across divergent
demographics, whereas the latter seeks to eliminate disparities in error rates among
these demographics. In contrast, individual fairness champions the principle oftreating similar individuals consistently.
▫ Proposed Solutions: This document elucidates diverse aspects of fairness, such as
demographic parity, equality of opportunity, and individual fairness. The former
entails the attainment of fair outcomes across diverse demographics, whereas thelatter aims to eliminate disparities in error rates among these entities. Conversely,
individual fairness advocates the principle of treating comparable individuals
consistently.
▫

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A. (2019). A survey on bias
and fairness in machine learning. arXiv preprint arXiv:1908.09635. Retrieved fromhttps://arxiv.org/abs/1908.09635
Approach to Fairness: This study examines the practical difficulties of incorporating
fairness into financial machine-learning models while simultaneously grappling withthe complex interplay between various fairness concepts.
▫ Definition of Fairness: This study examines various fairness measures, including
statistical parity, which advocates for equal treatment of all groups, and individual
fairness, which aims to provide consistent outcomes for individuals with similar
characteristics. Additionally, this study explores conditional statistical parity, which
considers legitimate factors that may warrant different treatment between groups.
▫ Proposed Solutions: The study's authors advocate for a practical methodology
wherein financial institutions must evaluate and weigh these competing fairness
criteria according to their scenarios. This could entail the development of tailoredfairness metrics that reconcile both the business objectives and regulatory
parameters of the financial sector.
▫

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Zhang, Y., & Zhou, L. (2019). Fairness Assessment for artificial intelligence in the Financial
Industry. arXiv preprint arXiv:1912.07211.
Approach to Fairness: This study presents a framework for evaluating the fairnessof AI within the financial sector, considering the distinctive data characteristics andregulatory requirements of the industry.
▫ Definition of Fairness: The framework incorporates multiple aspects of fairness,such as procedural fairness, which pertains to the methodology of decision-making,and outcome fairness, which focuses on the consequences of decisions. It recognizes
the necessity for fairness evaluations attuned to the intricacies of financial data andregulatory requirements.
▫ Proposed Solutions: The authors advocate for a tailored fairness assessment
approach that encompasses data collection, model development, and postdeployment evaluation for financial institutions that utilize AI systems. This
comprehensive approach is essential for guaranteeing fairness, transparency, andadherence to industry standards in the financial sector.
▫

United Nations Educational, Scientific and Cultural Organization. (2021).
Recommendation on the Ethics of Artificial Intelligence. [PDF file]. UNESCO.
https://unesco.org/EthicsAI
Approach to Fairness: This UNESCO document advocates for an inclusive, humanrights-centered approach to AI ethics, emphasizing fairness as a fundamental
principle. It highlights the necessity of developing AI systems that respect diversity,
promote non-discrimination, and ensure fair access to AI's benefits for all.
▫ Definition of Fairness: Fairness in this context is defined as the fair treatment of all
individuals and groups, ensuring that AI technologies do not replicate or exacerbate
biases and inequalities. It involves respecting human rights and fundamental
freedoms, with a focus on promoting social justice and inclusivity.
▫ Proposed Solutions: The UNESCO recommendation proposes several solutions toachieve fairness in AI. These include developing ethical governance frameworks,
ensuring transparency and explainability in AI systems, and promoting humanoversight and accountability. Additionally, the document emphasizes the need for
multi-stakeholder collaboration, adequate privacy and data protection measures, andthe assessment of AI's impact on various aspects of society, such as health, education,and the environment.
▫

Literature suggests a developmental trajectory in which fairness in AI has progressed froman abstract concept to a practical challenge, particularly in the financial services sector. Thishighlights the urgent need for a comprehensive interdisciplinary approach that combines
technical expertise with ethical foresight to address the complexity of fairness in AI. The
12

adoption of this multifaceted perspective is not only academic in nature but also crucial forthe ethical and responsible deployment of AI in the influential and far-reaching domains offinance.

3. Fairness: definition13

Sensu lato definition
In the world of AI, particularly within the B2B financial sector, the principle of fairness is afundamental aspect of responsible AI. This requires a sophisticated, multidimensional
approach rooted in both introspective analysis of AI systems and a comprehensive
understanding of the external business environment. Fairness in this context is more thanmere adherence to ethical standards; it also represents a dynamic and continual process oflearning and adaptation, which is essential for the interaction and decision-making
processes of AI systems in diverse business scenarios.
Central to this discourse is the fundamental principle of fair treatment of businesses that arealike in nature. In the intricate realm of financial artificial intelligence, this principle
mandates that algorithms assess businesses in an impartial and systematic manner. To
illustrate, businesses with comparable financial soundness or market hazards should be
evaluated uniformly using AI algorithms. This calls for AI systems to possess a profound anddiscerning comprehension of the business environment, allowing them to precisely discernand respond to delicate variations among company profiles.
In addition, the implementation of fairness in artificial intelligence presents significant
obstacles, precisely guaranteeing that AI models do not inadvertently produce outcomes that
disproportionately harm certain business groups. This concern demands extensive
observation and continuous monitoring of AI systems to detect and mitigate potential bias.
Consequently, the principle of fairness extends beyond the design of algorithms toencompass the entire lifecycle of AI systems, including data collection, model development,
deployment, and continuous refinement based on feedback.
Ensuring fairness in financial artificial intelligence necessitates a balance between predictive
accuracy and ethical considerations, particularly in financial decision-making, where theimplications of outcomes are significant. It is crucial to maintain a balance between highalgorithmic accuracy and comprehensive fairness protocols, a balance that is not constantbut evolves in response to modifications in the financial market and regulatory environment.

Sensu stricto definition
As we endeavor to examine fairness within AI, particularly in the B2B context, ourinvestigation now turns towards the critical topic of bias. This concept has been repeatedlyemphasized in scholarly literature on AI fairness. Bias in AI is essentially characterized by
two main forms: model bias and data bias. These two aspects of bias have a significant
influence on the fairness of AI systems, and understanding them is of great importance forthe creation of fair AI models in the financial sector.
The term model bias refers to the inherent inclinations within artificial intelligencealgorithms that can result in biased outcomes. This form of bias frequently stems from thedesign of the algorithm itself, where decision-making rules or standards may unintentionallyfavor specific groups over others. Model bias presents a daunting challenge because it can

14be discreetly integrated into the workings of AI systems, making it challenging to identify
and rectify.
On the other hand, data bias originates from the data scientists utilized to train artificial
intelligence models. It materializes when these datasets are not reflective of genuine world
circumstances, or when they comprise historical biases. In the B2B context, data bias canconsiderably influence AI's decision-making, resulting in unfair outcomes for specificbusiness entities, specifically those that are underrepresented or misrepresented in the
training data.

Fair process
Fairness, as a concept within the world of artificial intelligence in the B2B financial sector,
inherently possesses a dynamic nature that evolves in tandem with the market. This
characteristic necessitates that fairness be perceived not as a static target but as an
asymptote, an ever-approachable ideal that continually adapts to changing market
conditions. Consequently, what makes a fair transaction in this context is subject to thetemporal variability. A decision deemed fair today may no longer hold the same status
tomorrow, even when assessed using the same AI model with identical data. This fluidity
highlights the importance of continuously monitoring and updating AI systems to align withevolving market dynamics. As market conditions, business practices, and regulatory
landscapes shift, so do the parameters and assessments of fairness within AI systems,
ensuring that their decisions remain in accordance with the current state of the market. Thisapproach underscores the obligation of AI models used by the financial sector to embody aflexible and responsive framework that can adjust to the ever-changing definitions andstandards of fairness. Thus, the market is an ideal benchmark.
The pursuit of fairness in AI is an iterative and asymptotic endeavor characterized by ajourney rather than a final destination. This implies that AI systems are perpetually
immersed in the process of learning and adaptation, whereby they continually assimilate andapply fresh information regarding the multifarious financial entities they engage with. This
ongoing refinement process mirrors the intricate and ever-transforming nature of thefinancial market, thereby demanding AI systems capable of simultaneously evolving
alongside it.
An essential aspect of this endeavor is the AI system's extensive grasp of a variety of businessmodels and market trends. This necessitates the merging of advanced AI algorithms with athorough understanding of diverse business types, their financial performance indicators,
and the challenges associated with each market participant. Establishing such a refined
understanding is crucial to guarantee that AI-guided decisions are fair and customized to thespecific attributes of each financial entity.
Furthermore, the concept of fairness in AI has been extended to include proactive bias
management. This entails a vigilant and adaptive approach for identifying and mitigatingbiases, both within the AI models and the underlying data. Recognizing that biases are
dynamic, AI systems must be equipped to respond in real time and continuously update theirprocesses and datasets to maintain fairness.

15

Lastly, a growing academic discourse1 suggests a potential correlation between fairness in
AI and improved business performance. This theory posits that a deeper understanding ofbusiness counterparties, facilitated by fair AI systems, can lead to more effective andinformed decision making. Such decision-making could in turn enhance the performance andprofitability of AI-driven financial services, underscoring the multifaceted benefits offairness in AI.

Data bias
Data bias in AI, particularly in the context of the B2B financial sector, is a critical issue thatarises when the datasets used to train and inform AI systems are not fully representative of
diverse market actors or are not updated in real time to reflect current market conditions.
This type of bias can significantly skew the AI-driven decision-making processes, leading to
unfair or inaccurate outcomes.

Origin of data bias
Data bias arises when the data utilized in the training of an AI model do not represent thediversity of the market or are outdated, which can lead to inaccuracies in the AI learning
process. In the B2B financial sector, this may manifest as the absence of sufficient and up-todate data from certain types of businesses, such as niche economic agents or emerging
industries, in the training of a credit-scoring model. Consequently, the AI system may bebiased towards the characteristics of more well-represented entities, such as systemicbanks.

Manifestation of data bias
Data bias can result in the discriminatory treatment of certain market participants. An
illustration of this can be seen in loan approval AI that has primarily been trained on datafrom systemic banks. In such a scenario, AI may underestimate or misconstrue the financial
well-being of less significant banks, consequently leading to unfair credit determinations.

Impact of data bias
The ramifications of the data bias are substantial. This can result in systemic disparities
where specific businesses, such as less significant banks, are constantly at a disadvantage.
This issue extends beyond individual financial entities, and has the potential to skew market
competition and compromise financial stability.

Model Bias
Model bias in AI refers to inherent predispositions within AI algorithms that lead to skewed
or prejudiced outcomes. This bias arises from the underlying mechanisms and structures ofAI models, which may inadvertently favor certain patterns, characteristics, or groups over
Frontiers Editorial Office. (2020). The impact of artificial intelligence on firm performance: An
application of the resource-based view to e-commerce firms. Frontiers in Psychology, 11, 580406.
https://doi.org/10.3389/fpsyg.2020.580406
1

16others, leading to discriminatory decisions. Understanding model bias is crucial in thecontext of AI-driven financial services, as such biases can have significant and far-reaching
implications.

Origin of model bias
Model bias often originates from the manner in which AI algorithms are designed andtrained. These biases can be introduced through various factors, including the subjective
decisions of the model developers, algorithmic structure, or choice of features andparameters used in the model. For instance, if an AI system for credit scoring is
predominantly trained on data from large corporations, it may be less accurate or fair whenassessing smaller businesses because of its overfitting to the characteristics of larger entities.

Manifestation of model bias
In practical applications, model bias is revealed through a systematic inclination of AI
systems to make decisions that apply unfairness towards specific groups. This may manifest
as a persistent disadvantage experienced by specific types of business. Bias can also stemfrom the learning algorithms themselves. For instance, if an algorithm is designed toprioritize certain financial indicators that are more common in larger banks, it inadvertently
discriminates against smaller banks.

Impact of model bias
The impact of model bias can lead to a perpetuation of existing unfairness2, where someentities are unduly favored or penalized, not based on their actual performance or risk
profile; but due to the biased nature of AI models. This not only affects individual businesses,
but also has broader implications for market dynamics and industry competition.
Hence, the goal of achieving de facto fairness in the B2B context is achieved by integrating afair process with a controlled model and data biases, as previously presented. Controllingmodel bias involves calibrating the AI to prevent any inherent bias in decision-making,whereas managing data bias necessitates the AI to be trained on diverse and current
datasets. Collectively, these components culminate in de facto fairness, in which thefunctionality of AI is inherently fair in practical applications in the market, reflecting an
optimal equilibrium between ethical standards and functional feasibility in the financial
services industry.

2

Das, S., Donini, M., Gelman, J., Haas, K., Hardt, M., Katzman, J., ... & Zafar, M. B. (2021). Fairness
measures for machine learning in finance. The Journal of Financial Data Science.

17

4. FinFair AI Index framework:
methodology
18Description
Artificial Intelligence has ushered in a significant transformation in the financial sector, notonly in terms of technological progress, but also in the socio-economic principles that
underpin this domain. This study endeavors to explore this transformative phase, drawing
on Durkheim's theoretical perspective and, specifically, the manifestation of mechanical andorganic solidarity in the context of the financial sector's adoption of AI, with a focus on B2B
fairness scoring models.
Traditional financial models, which have historically served as the foundational framework
for risk assessment and decision-making processes in the sector, have long beencharacterized by a focus on mechanical solidarity. These models often reflect thehomogeneity and shared norms that typify this approach, and may not adequately address
the diverse needs of various business entities. However, the integration of AI, particularly
fairness-oriented scoring models, has ushered in a compelling shift towards a fairer
approach. This transition is not merely a technological upgrade, but represents a significant
change in the organic solidarity of the financial sector.
The application of AI fairness models in a B2B context challenges conventional notions ofmechanical solidarity and necessitates a reevaluation of established norms and practices.
This shift towards organic solidarity is characterized by a more inclusive approach. This
research contends that the development and implementation of AI models are not merely atransition from mechanical solidarity to organic solidarity, but rather an evolution towards
a system that acknowledges and corrects the disparities and biases inherent in traditional
financial models. This movement aligns with an ethically driven practice that prioritizes
fairness.
The AI fairness scoring model proposed in this study is not an individual tool but rather form
part of a comprehensive collaborative framework that involves public authorities andbusinesses. This cooperation is essential for aligning models with the ever-changing
landscape of businesses and their dedication to fairness. By adopting this approach, we canensure a scoring system that is better suited to the unique context of individual businesses
and reflects a shift in how financial risks and opportunities are assessed and managed,transitioning from mechanical to organic solidarity.
In essence, this study investigates the transformation of the financial sector through AIdriven fairness models, using Durkheim's concept of mechanical and organic solidarity3. Itemphasizes the need for a holistic transformation that extends beyond technicaladvancements to encompass a reform of cohesion and institutional practices, paving the way
for a fairer financial ecosystem by promoting the adoption of fair AI models within the B2B
sphere.

3

Durkheim, E. (2010). From mechanical to organic solidarity. Sociology: Introductory Readings, 2(1), 25-29.

19

Organic Solidarity vs Mechanic Solidarity
The concepts of mechanical and organic solidarity, as elucidated by Émile Durkheim, providea refined perspective on fairness and decision-making processes in the business-to-businessfinancial sector when applied to artificial intelligence. . Mechanical solidarity, characterizedby homogeneity and a set of shared norms, typically underpins the conventional reporting
frameworks between banks and regulators, aimed at promoting a stable financial system.
This framework comprises the ensemble of norms that bind the regulator to the bank,focusing primarily on the relationship between these two entities without considering thebroader interactions among banks in the market. The existing financial risk assessment
models, rooted in the principles of mechanical solidarity, often overlook the diversity anddistinctiveness of business entities within the financial market
Consequently, these models tend to rely on a standardized approach to evaluations,perpetuating a one-size-fits-all mentality that does not adequately address the nuances ofindividual entities. This approach, while providing a degree of consistency and predictability
in regulatory reporting, often fails to capture the complexities and specificities inherent ininter-bank dynamics.
A fairness-centric scoring models heralds a profound evolution towards organic solidarity
within the financial domain. This evolution marks a departure from uniform methodologies,
ushering in an era that acknowledges and aligns with the intricate interdependencies offinancial institutions. Such a tailored approach gains paramount importance in the B2B
sphere, where the interplay and transactions between entities, like banks, are critical. Thistransition encapsulates the quintessence of organic solidarity in the financial ecosystem,
reflecting a more nuanced and interconnected approach to financial interactions andassessments.
In a more nuanced perspective, organic solidarity within the financial sector is underpinnedby the intricate interplay among banking institutions. A pivotal element in this dynamic is
the mitigation of endogenous factors that frequently trigger systemic anomalies. This goaltranscends the scope of standard regulatory reporting, characteristic of orthodox financial
regulations, and delves into the enhancement of symbiotic inter-bank relationships. Central
to this pursuit is the necessity to refine the fairness of AI systems, which are integral to thefoundational interactions among market participants in the financial arena.
A FinFair AI Index in the B2B context fosters a dual responsibility framework: one thatencompasses both regulatory bodies and inter-bank dynamics. This paradigm shift leads toa more robust operational process, transitioning from a one-to-many (regulator to individualbank) approach to a many-to-many framework4. This holistic model not only promotes
accountability but also enhances the resilience of the financial system in terms of fairness
promotion.
Moreover, this approach proves to be cost-effective, as it inherently reduces the likelihoodof a general financial crisis. It is essential to recall that global financial crises are often the
result of inefficiencies within the system – a manifestation of endogenous malfunctions. The
4bank-to-bank and bank-to-regulator

20interaction between banks, especially when utilizing AI in B2B transactions, plays a crucialrole in mitigating these endogenous constraints. It allows for a more accurate assessment of
the fairness of AI systems employed by banks, thereby contributing to a more efficient andfair financial ecosystem.
In essence, by fostering a network of interactions underpinned by fair AI systems, thefinancial sector can move towards a more interconnected and resilient framework. This not
only enhances the overall efficiency of the system but also aligns with the principles oforganic solidarity, ensuring that each entity's actions contribute positively to the stability
and sustainability of the broader financial market.
In the business-to-business financial sector, organic solidarity is of immense importance. It
acknowledges the network of connections among the various financial institutions. For
example, in a transaction involving artificial in predicting fraud, it is essential that Bank A
does not unfairly discriminate against Bank B, and vice versa. This interconnectedness
necessitates a refined comprehension and utilization of AI models that can discern thesebusiness relationships.
The concept of organic solidarity enables a specific and balanced methodology for thefinancial industry. It fosters the creation of models that not only exhibit uniform fairness but
also address individual fairness. This will be elucidated in future research throughdelineation of the FinFair AI Index equation, which seeks to quantify and embody thisconcept of fairness in an operational manner.
Building upon our defined concept of fairness in AI within the B2B financial sector, we now
delineate a methodology for developing a fairness scoring system in this context, drawing
inspiration from the structural functionalism paradigm. This theory, contributed to by
Spencer, Merton and even Durkheim, views interactions as part of a complex system where
various parties collaborate to maintain stability, enhance accuracy in the AI predictions andpromote fairness. This theoretical lens provides a valuable framework to understand andenhance AI fairness in the financial sector.

Structural functionalist paradigm
This theoretical perspective conceives of the financial sector as a collective of interconnected
entities comprising businesses, regulatory bodies, financial institutions, and AI technologies.
Each entity assumes a unique role: collectively, it contributes to the sector's operational
efficiency. By adopting this approach, one can examine the intricate web of relationships
within a sector and how these relationships shape the pursuit of fairness.
The objective is to evolve the structure of the B2B financial sector to realize factual fairness(de facto fairness). This evolution involved the introduction of an incentive-based fairnessscoring model. This model is designed to measure fairness and foster practices that promote
fair outcomes in AI-driven decisions. This scoring system is envisioned to be fair in its
process, thus incentivizing the adoption of fair AI practices across the sector.

Fairness scoring model development
Our methodology, rooted in the principles of structural functionalism, seeks to establish acomprehensive framework within the B2B financial sector that emphasizes fairness in AI.
21This approach entails a fair and transparent process to implement a fairness scoring systemdesigned to elevate fairness scores and foster a continuous cycle of improvement. By
collaborating and adhering to the principles of fair AI, financial entities can actively
contribute to creating an environment in which fairness is realized and reinforced. This
framework evaluates current fairness levels and encourages the adoption of practices thatenhance fairness, thereby promoting the financial sector's communion, the latest being theprerequisite of de facto fairness
As we continue to refine our methodology for developing a fairness scoring system for AI in
the B2B financial sector, we delve deeper into the structural functionalism paradigm. This
paradigm draws on Émile Durkheim's concept of mechanical and organic solidarity within afunctioning system, offering a nuanced understanding of systemic cohesion and theevolution of social structures. By adapting Durkheim's framework, we explore thetransformation of standard financial practices towards a fairer and inclusive AI-driven
environment.

From mechanical to organic solidarity in financeDurkheim's theory of mechanical solidarity aligns with the traditional standardized risk
management models prevalent in the financial sector, such as the Internal Ratings-Based(IRB) approach used by banks. While forming the technical backbone of the sector, thesemodels often reflect and perpetuate existing power dynamics, favoring institutions withmore extensive data and resources. This scenario is akin to mechanical solidarity, in which
the homogeneity of practices and perspectives dominates.
In contrast, our proposed FinFair AI Index model signifies a concrete shift towards organic
solidarity, embodying a move away from uniformity towards a system that values diversity
and interdependence. This model represents not only technical recalibration but alsobroader cultural and ideological evolution in the financial sector. It advocates a morenuanced and fair approach to AI evaluation, recognizing the importance of the process ofachieving fairness alongside the outcome.

Collaboration between public authorities and financial players
The core of this model is cooperation between public authorities and financial institutions.
This partnership is essential for the shift from mechanical solidarity, which relies on
standardized models, to organic solidarity, which is characterized by de facto fairness. By
enabling banks to modify their AI models to reflect their evolving commitment to fair
practices, public authorities can contribute significantly to this transition. This collaboration
ensures that fairness scoring is not only more tailored and pertinent, but also inspires banks
to strive for fairness through a collaborative and calibrated strategy. Finally, the fairness
scoring system was designed to be adaptable and personalized, acknowledging the uniquecontexts and capabilities of different banks. This approach allows for a more tailoredassessment of AI systems and data, promoting fairness in the AI applications.

FinFair AI Index framework
We propose a new equation to evolve the structure of the B2B financial sector towards
actualized fairness: the FinFair AI Index. This model focuses on the development of an
incentive-based and inherently fair AI fairness scoring system. Our approach seeks to22

embrace a new paradigm that first thoroughly comprehends the current structure of thefinancial sector and then responds to it using a specially formulated equation aimed atachieving fairness objectives. We are now advancing towards the practical realization of thepreviously discussed theoretical approach by implementing a non-competitive andindividual fairness scoring. This scoring takes into account the interaction between banks
and is based on a collaborative effort with regulatory authorities.
The equation developed in this study was not intended for random or arbitrary applications.
Rather, it is designed for implementation within a framework founded on public-private
collaboration, as the outcome of the scoring is inherently linked to the organization andquality of this collaboration. This represents a novel approach to our research. Additionally,
this framework facilitates the positive evolution of the scoring process, incentivizing banks
to improve their fairness scores. The weights and values assigned to the specific variables inthe equation are critical for demonstrating this aspect. These are elements in whichbusinesses have less flexibility and must provide justifications. It also underscores thefairness of the adjustable application of the fairness model. Consequently, any random
testing of public data would inherently be unfair, as it would be disconnected from theestablished framework. This approach allows us to move away from arbitrary standardized
models or internal banking models that often do not reflect reality within conventionalframeworks.
When constructing the equation, it is essential to delineate and articulate the variables that
contribute to the integrity of AI systems. The blueprint for this endeavor starts with the "fairprocess" dimension, a systematic protocol that refines AI practices to ensure fair outcomes.
Within this process, pivotal junctures are encountered, each of which presents criticalvariables that require attention.
Firstly, we address model bias, a variable that encapsulates the predispositions embedded
within algorithmic decisions. This bias can manifest through historical and structuralinequalities that the model may inadvertently learn and perpetuate. Understanding andmitigating model bias involves examining the design of the algorithm and the assumptions
underlying its predictive judgments. Subsequently, we shifted our focus to "data bias," avariable deeply entrenched in the data that serves as the foundation for machine learning.
The representativeness and quality of the data are paramount, as any oversight can lead toAI systems that reinforce existing disparities. Data bias is often a reflection of the financial
sector as it is, not as it should be, and identifying this discrepancy is crucial for theadvancement of fair AI practices.

23

Understanding the systematic unraveling of these variables and their development paves theway for their calibration. The path we embark on necessitates a diligent presentation of eachvariable, guaranteeing that our trajectory adheres to the overarching objective of fairness.
0. Final equation:

Fairness scoring ranged from 0 to 10. A score closer to 10 indicated that the AI used in B2B
was fair. Conversely, a score closer to 0 suggests that the AI is less fair:f : A→[0,10],
where A denotes the set of AI systems under consideration. Function f assigns to each AI
system a ∈ A a real number s, which represents its fairness score. A higher value (closer to10) implies a higher fairness of the AI model, whereas a lower value (closer to 0) implies alower fairness of the AI system.
For example, if a is an AI system with perfect fairness, then f(a)=10. Conversely, if a is the AI
system with the lowest possible fairness, f(a)=0.
1. Fair process: variables
Base score
The calibration and engagement aspects within our fairness scoring framework are crucial
for its effective implementation in AI systems used by financial institutions, particularly in
the B2B sector. This facet of the model hinges on the dynamic relationship between the basescore and concrete commitments of banks to enhance fairness, specifically in areas such as
Fa (awareness factor) and Fd (documentation factor). The base score, initially set to 1, serves
as a benchmark for assessing the initial level of fairness in AI models used by banks orfinancial institutions.
As financial institutions progress in their fairness initiatives, as reflected in improvements
in factors such as Fa and Fd, the base score is designed to respond proportionally. Publicauthorities and regulatory bodies play a pivotal role in this process, with the discretion toadjust the base score in subsequent evaluations. For instance, one year after the initial
24

assessment (at time t+1), if a bank demonstrates substantial improvements in integrating
fairness into its AI systems, the base score can be increased accordingly. This increase ismeant to be a direct reflection of banks’ engagement and advancements in fairness.
Conversely, if there is no significant progress or decrease in engagement, the base scoreremains at its initial value, ensuring that institutions are neither unfairly penalized nor
rewarded without merit.
An important characteristic of this model is its individualized and non-comparative nature.
The FinFair AI Index, which measures AI fairness in the B2B financial sector, is a standardized
and individualized measure. It provides a consistent framework for fairness assessment,
while acknowledging the unique circumstances and efforts of each bank. Consequently, thisprecludes a direct comparison of scores or base scores between banks. The focus is onindividual improvement and progress in fairness rather than competitive benchmarking.
Moreover, this approach encapsulates the symbiotic partnership between financial
institutions' dedication to de facto fairness, and the guiding role of public authorities. By
dynamically calibrating the base score in response to each bank's fairness efforts, the scoringmodel remains a relevant, effective, and equitable tool to advance responsible AI practices
in the financial sector.
In our research, the concept of a base score within the fairness scoring equation plays apivotal role, symbolizing the calibration of scoring in alignment with banks’ commitment tofairness in their operations. This approach underscores a key aspect of our methodology:
incentivizing and recognizing the efforts of financial institutions to achieve fairness in their
AI systems.
Let B denote the base score. Mathematically, B is defined as a function of various fairnessrelated variables and their respective improvements over time. Specifically, the base scorecan be formulated as

Where:▫ B(t) is the base score at time t.
▫ B(t+1) is the base score at time t+1 (e.g., after a year).
▫ Δi (t) represents the incremental change in the base score at time t owing to
improvements in the i-th fairness-related variable.
▫ n is the total number of fairness-related variables considered in the model (such as
Fa and Fd).
The incremental change Δi (t) for each variable was determined based on the specific criteria
established for that variable's contribution to overall fairness. For instance, improvements
in Fa or Fd would lead to a positive increase in Δi (t), enhancing the base score. This
formulation allows for dynamic adjustment of the base score in response to ongoing
improvements in AI fairness by financial institutions.

25

2. Data bias: variables
Awareness factor: Fa

To delve deeper into the role of Fa (awareness factor) in assessing AI fairness in the B2B
financial sector, let us consider its significance and technical application in the context of thisresearch.

Fa represents the degree of awareness and proactive measures an organization has
regarding biases in AI systems (webinars, trainings, etc.)▫ Mathematically, this is represented as (1+α×Fa) in the fairness equation. Here, α is acoefficient that amplifies the effect of Fa on the overall fairness score. This
formulation suggests that a higher awareness correlates with a higher fairness score,acknowledging efforts to identify and mitigate biases.
▫ α is a coefficient that amplifies the effect of Fa on the overall fairness score. Thiscoefficient signifies the extent to which the organization's awareness and efforts toaddress biases contribute to the fairness of the AI system.
▫ This approach also allows human involvement in the fairness process, as it is essentialthat humans are engaged in preventing the FinFair AI Index from becoming an
exclusively technical matter. In addition, the OECD principles5 on AI emphasize theintegration of human oversight to ensure fairness, aligning AI systems with human
implications. This included the implementation of human intervention and oversightmechanisms. In the B2B financial sector, adhering to these standards reassuresfairness and aligns with auditing requirements and compliance.
▫

Importance in the financial sector▫ In B2B finance, decisions made by AI systems, such as loan approvals or risk
assessments, have significant economic and social impacts. An elevated Fa
indicates a financial institution's commitment to understanding andaddressing potential biases in AI-driven decisions.
▫ This is crucial because biases in AI can lead to unfair practices, affecting businesses
and economies. A high Fa score implies that the institution not only acknowledges
these risks, but also actively engages in practices to ensure that their AI systems
are as unbiased as possible.
Fairness approach: The awareness factor (Fa) plays a critical role in mitigating therisks associated with overreliance on mathematical models. This factor quantifies theextent of organizational awareness and proactive measures against biases in AI
5

OECD. (n.d.). Human-centred values and fairness (Principle 1.2). Human-centred values and fairness(OECD AI Principle) - OECD.AI. https://oecd.ai/en/dashboards/ai-principles/P6

26

systems, encompassing regular employee training and internal surveys. The inclusionof Fa in the fairness equation, often represented as (1+α×Fa), highlights the balance
between mathematical quantification and human expertise. This approach counters
the trend of overmathematisation, which historically emphasized reliance solely onmodels without human oversight, a stance epitomized by eminent figures such as
Milton Friedman. The incorporation of Fa aligns with the principles of responsible AI,
advocating a blend of human expertise and quantitative analysis to achieve fairness
in AI applications.
In addition, the awareness factor (Fa) was assigned a weight to underscore theintegral role of human discretion in the fairness evaluation process. This approach
recognizes that fairness is not solely a product of models and data but encompasses
the entire process, including human oversight. Reimagining fairness involves thesynthesis of algorithmic logic, data, and public–private partnerships. Mandating
corporate self-assessment in terms of fairness can drive improvement, as models
inherently cannot fully capture fairness. The weight increase for Fa, particularly for
efforts in biased documentation and awareness, signifies the long-term investment ineducation for the success of fair AI in B2B contexts. This evolution of fairness,transcending technological confines, advocates collaborative approaches to holistic
and responsible AI development.

Documentation factor: Fd

In examining the role of the documentation factor (Fd) within the context of AI fairness
in B2B financial services, it is pertinent to recognize its function as a quantifiable
metric of an organization's commitment to recording and understanding biases thatemerge during AI utilization.

Fd encapsulates the degree to which a financial institution document encounters bias
in its AI system. Mathematically, this is denoted as (1+β×Fd) within the fairness
equation, where β serves as the scaling coefficient that influences the impact of
documentation efforts on the overall fairness score. Thus, the inclusion of Fd directly
correlates an institution’s dedication to transparency and accountability with its
enhanced fairness score.
Importance in the financial sector: In the B2B finance realm, AI systems are pivotal
in critical decision-making processes. Documentation of biases is not just areactionary measure but also a proactive strategy. A robust Fd score indicates that afinancial institution is not only aware of potential biases, but is also actively
documenting these issues, which is vital in the iterative process of AI improvement.
This commitment to documentation assists in navigating through the complexities ofbiases, ensuring that AI systems evolve to become fairer over time.

27

Fairness Approach: The index's novel incorporation of Fd reflects a comprehensive
approach to fairness that surpasses that of traditional methods. It embodies apartnership between public regulatory bodies and private financial entities,
advocating for calibrated measures of fairness that are contingent upon the size andtechnological prowess of the institution in question. The introduction of a weight, forFd, could represent this nuanced calibration, aligning with a more sophisticated,
multidimensional view of fairness that integrates human-driven documentation as acounterbalance to the potential 'overmathematization' of fairness.
Shannon Index

Incorporating the normalized Shannon index into the fairness equation is a significant
advancement in assessing data diversity in AI systems, especially in the B2B financial
context. This integration is a synthesis of theoretical understanding and practicalapplications, highlighting the crucial role of diverse data in facilitating fair AI decisionmaking processes.

Where:
H is the Shannon index.
where n denotes the number of categories (or classes) in the dataset.
pi is the proportion of the dataset belonging to the ith category.
▫ The summation (∑) was performed for all categories in the dataset.
▫ log(pi ) is the natural logarithm of proportion pi .
▫
▫
▫

To normalize the Shannon index, we adjusted it to fall within a specific range, typicallybetween 0 and 1. This is done by dividing the Shannon Index by the logarithm of thenumber of categories (log(n)), which is the maximum possible value under theassumption of equal distribution across categories:

The Shannon index, especially when normalized, quantifies the diversity and richnessof a dataset. It calculates the proportional representation of each feature in thedataset. In our context, the features of this scoring system include the proportional
representation of various financial actors4 within the market. For instance, if a bank's
transactional data with these financial actors are proportionally reflected in thedatasets used to train the AI, it could potentially lead to a higher fairness score.
Integrating this measure into the fairness equation is theoretically justified by thepremise that greater data diversity leads to fairer AI systems. This diversity ensures
exposure to various scenarios and conditions, thereby mitigating the biases inherent
in more homogeneous datasets.
28

The Shannon index was computed as follows:
H′=−log(n)∑(pi ⋅log(pi ))where pi represents the proportion of each category, and n is the total numberof categories.
Range of the Shannon index: The normalized Shannon Index ranges from 0 (nodiversity) to 1 (maximum diversity). A higher value of H′ indicated greater diversity
within the dataset.
In summary, the inclusion of the normalized Shannon Index in the fairness equation
is a methodological innovation that emphasizes the essential role of data diversity in
AI fairness assessments. This provides a quantifiable measure to address the issue ofdata bias.
3. Model bias: variables
Statistical Parity Difference (SPD)The Statistical Parity Difference (SPD) is an important metric in the fairness equationthat gauges the balance of positive outcomes between different groups. It isparticularly vital in B2B financial settings, where the AI's decision-making process
may inadvertently favor certain types of businesses over others due to historical data
trends or inherent algorithmic biases.

Normalized SPD is calculated by taking the absolute difference in positive outcome
rates between two distinct groups, divided by the maximum possible disparity.
Mathematically, it can be represented as:
Where:▫ P(Y=1∣G1) is the probability of a positive outcome for the privileged group G1.
▫ P(Y=1∣G2) is the probability of a positive outcome for the disadvantaged group
G2.
▫ The absolute difference
inthese probabilities isdivided by
1−min(P(Y=1∣G1),P(Y=1∣G2)), which normalizes the SPD.

29

Range of SPDnormalized: The range of normalized SPD is between 0 and 1, where 0
indicates perfect parity between groups and 1 indicates the maximum possible
disparity. A low value indicates little to no disparity in positive outcomes between
groups, whereas a higher value indicates potential bias in the AI system that needs tobe addressed.
Importance in the financial sector: For financial institutions, a fair AI system is one
that does not skew its decision-making process based on the size of the bank, industry
sector, or geographical location. Maintaining a low normalized SPD is vital to ensurethat all businesses have equal treatment, regardless of the group.
Fairness approach: The inclusion of normalized SPD in the fairness score calculationreflects an advanced approach to ensure that AI models in the B2B context do not perpetuate
or exacerbate existing inequalities. By striving to minimize normalized SPD, financial
institutions can demonstrate their commitment to fair treatment and decision-making
towards their counterparties, fostering a more trusted and fairer financial ecosystem.
Parameter K

Parameter k in the fairness equation encapsulates the equilibrium state of the fairness
assessment, serving as a statistical baseline against which the impact of SPD normalizationis measured. This represents the average or expected level of SPD normalized across thedataset, ensuring that the fairness score is grounded in the historical context of the AI
system’s decisions. Consequently, the selection of k is a strategic choice that aligns with theprinciple of maintaining a balanced and objective framework for evaluating fairness,ensuring that the model's assessment begins from a neutral standpoint before accounting
for the variability introduced by the SPD. This neutrality is pivotal because it avoids skewing
the fairness score towards either extreme of the dataset and allows for a fair comparison
across different AI models and applications in the financial sector.
In the context of the fairness equation, parameter k is adapted as the FinFair AI Index evolves.
The selection of k was based on several principles to ensure the robustness and relevance ofthe fairness score.
Avoiding division by zero: k must be positive and sufficiently large to prevent
division by a value close to zero when the normalized SPD is very low.
▫ Preserving the score scale: The magnitude of k is critical; it must be calibrated suchthat the fairness score remains within the range of 0 to 10. If k is too small, variations
in SPD will disproportionately influence the score; if k is too large, the impact of SPD
becomes negligible.
▫ Base neutrality: k may be set at a value that reflects a neutral starting point for
fairness before considering the SPD.
▫ Statistical analysis: k can be determined based on historical fairness scores,
calibrated such that the distribution of scores is centered around a value representing
fairness.
▫

30

Essentially, k should be congruent with a bank’s historical data to prevent biasing theequation, allowing for a personalized result that is truly fair. For a rational choice of k, one
might consider the mean or median of the historical or projected values for SPDnormalized,
or even half the range of SPD normalized if it varies from 0 to 1. This establishes k as a pointof equilibrium: below it, SPDnormalized enhances the fairness score relatively; above it, theeffect is reductive. Using the mean or median ensures that k is representative of what is
typical for the dataset, and hence, neutral.
Disparity Ratio (DR)Disparity Ratio (DR) is a critical metric for evaluating fairness in AI systems, particularly inthe financial industry. As a measure, it examines the essential question of whether the AI
system treats different groups fairly when making decisions.
DR's significance stems from its ability to quantify disparities in positive outcomes between
groups. AI systems might assess loan applications or detect fraudulent activities, ensuringthat all types of banks are treated fairly. A disproportionate positive outcome rate, indicatedby a DR significantly different from 1, could reveal inherent bias in the AI system.
The significance of DR in fairness scoring is two-fold. First, it aids in detecting potential
biases present in the system. Second, it serves as a measure to guide rectifications in the AI
models. By aiming for a DR value close to 1, financial institutions can improve the fairness of
their AI systems, ensuring that decisions are made impartially across diverse groups.
DR is mathematically articulated as the ratio of positive outcomes (such as accurate fraud
detection) between privileged (Group A) and unprivileged groups (Group B). DR is calculated
as:

Where:▫ P(Y=1∣Group A) represents the probability of a positive outcome (such as correct
fraud detection) for Group A.
▫ P(Y=1∣Group B) represents the same for Group B.
The term "positive outcomes" refers to AI's accurate detection of fraudulent activity. DR isused to measure the relative effectiveness of the AI in different groups, such as those definedby business type or geographical location.
Range of DR: The DR value ranges from 0 to infinity. A value of 1 indicates no disparity,
values less than 1 suggest fewer positive outcomes for the numerator group, and values
greater than 1 indicate a potential bias in favor of that group.
Fairness approach: From a scientific perspective, DR addresses a gap often overlooked in
traditional fairness metrics: the differential impact of AI decisions across diverse business
entities. By quantifying the ratio of positive outcomes between the groups, DR challenges the31notion that a universally fair AI system can be achieved solely through accuracy andprecision. For instance, an AI system in finance might show high accuracy in fraud detection;
however, if this accuracy disproportionately favors larger enterprises over smaller ones, thesystem can hardly be deemed fair.
The deeper implication of DR in fairness scoring presents a subversive yet essential critiqueof the prevailing AI practices. It calls upon us to grapple with and rectify instances where AI
professes objectivity and inadvertently perpetuates systemic biases. In the realm of finance,
AI models are reassessed to prevent them from inherently favoring specific types ofbusinesses, such as those with copious data availability, extended financial histories,
overburgeoning entities, or new counterparties that may contribute innovatively to themarket.
Predictive Parity (PP)

Predictive Parity (PP) in AI fairness, especially in applications such as anti-money launderingand fraud detection, is a key metric for evaluating the fair performance of algorithms. Its rolein a comprehensive fairness equation is to ensure that AI predictions are accurate across all
analyzed groups.
Predictive Parity is achieved when the probability of a positive prediction (such as detecting
a fraudulent transaction) being correct remains consistent across all groups. For instance, if
an AI system examines transactions from small banks (Group A) and systemic banks (GroupB) and marks certain transactions as suspicious, the PP would require that the true positive
rate, the percentage of correctly identified fraudulent transactions, be equivalent for bothgroups.
Mathematically, PP can be expressed as the ratio of true positive rates between privileged
groups (Group A) and unprivileged groups (Group B):

Where TPR 𝐺𝐴

and TPR 𝐺𝐵

are the true positive rates for groups A and B, respectively.

Fairness approach: Achieving Predictive Parity is inevitable for ensuring fairness in fraud
detection. If one group has a significantly different true positive rate, it could indicate a bias
in the AI model, favoring or disadvantaging one counterparty over another. Therefore, abalanced PP suggests that AI's predictions are reliably fair, regardless of the business type.
In the context of a fairness equation, PP complements DR by offering an alternate
perspective. While DR assesses the balance of positive outcomes, PP focuses on the accuracyof positive predictions. It is crucial to consider both DR and PP in our FinFair AI Index toprovide a holistic view of AI performance. Balancing these metrics ensures that no aspect offairness is overlooked, thereby achieving a more comprehensive and fair AI system.

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False Negative Ratio (FNR)

The False Negative Rate (FNR) is a metric for assessing the fairness and accuracy of AI
systems, especially in the financial sector. It was calculated as the ratio of false negatives tothe total number of actual positive cases. Mathematically, the FNR is represented as:

Range of FPR: An FNR of 0 (min) signifies no false positive errors, whereas an FNR of 1(max) indicates that all negative cases are incorrectly identified.
Fairness approach: Achieving a low False Negative Rate (FNR) is of paramount importancefor fairness in AI systems, particularly in financial services, such as loan processing or fraud
detection. A high FNR could indicate that the AI system erroneously rejects legitimate cases,
such as denying loans to qualified customers or failing to detect genuine transactions. Thiscan result in the unfair treatment of certain individuals or groups, potentially exacerbating
existing biases. To guarantee fairness, it is imperative that the AI system minimize the FNR,
thereby accurately identifying true positive cases. Incorporating this metric into the fairness
equation enables a nuanced assessment of the model's sensitivity and capacity to refrainfrom unfairly penalizing legitimate cases.
False Positive Ratio (FPR)

False Positive Rate (FPR) is a metric for evaluating both fairness and accuracy. The FPR
assesses how frequently an AI model incorrectly predicts a positive outcome for factuallynegative cases. Here, is the mathematical formulation of FPR:

Range of FPR: An FPR of 0 (min) signifies no false negative errors, whereas an FPR of 1(max) indicates that all positive cases are incorrectly identified.
Fairness approach: Similarly, maintaining a low False Positive Rate (FPR) is vital for fair AI
decision making. In scenarios such as anti-fraud measures, a high FPR might result in falsely
flagging legitimate activities as fraudulent, leading to unfair practices to counterparties. Suchbiases could disproportionately affect certain groups depending on the characteristics of thedata on which the AI system was trained. Thus, the fairness approach involves rigorously
fine-tuning the AI model to reduce FPR, ensuring that it does not unduly impact any group.
Balancing the FPR with other fairness metrics is key to developing AI systems that are bothaccurate and fair.
4. Model bias: Groups33

In addressing model biases, particularly after covering all variables of the FinFair AI Index, it
becomes crucial to identify and categorize the groups upon which the framework will beapplied. This entails calculating key fairness metrics such as the Statistical Parity Difference(SPD), Disparity Ratio (DR), Predictive Parity (PP), False Negative Rate (FNR), and False
Positive Rate (FPR). These metrics serve as fundamental tools for assessing and comparing
the performances of AI models across different groups. Comparing these groups is a criticalcommitment that banks must undertake to combat model bias. It is not just about
recognizing disparities, but also actively engaging in their mitigation. To facilitate thisprocess, we established a table outlining the groups and subgroups. This table serves as astructured guide for banks to systematically examine and address the biases within their AI
models. By rigorously analyzing these metrics, financial institutions can ensure that their AI
systems are fairer, thereby aligning with the broader goal of reducing bias in model outputs.

Category
Subcategory
1. Transaction type 1.A Domestic vs.
GroupsInternational

Definition
Transactions within
a single country vs.
across borders.

1.B Interbank vs.
CustomerTransactions
between banks
versus thosebetween banks andtheir clients.

1.C Payment vs.
CreditImmediate paymenttransfers versus
transactions that
extend credit.

1.D High-Value vs.
Low-ValueGrouping is basedon the monetary
value oftransactions.

2.A Europe (EUand Non-EU)

Transactions within
EU countries vs.
non-EU European
countries.

2. Geographic
GroupsRationale
Different fraud
patterns due tovarying regulatoryenvironments andcurrency risks.
Different risk
profiles andtransaction scales
between interbank
and customertransactions.
Complexity in risk
assessment differs
betweenstraightforward andcredit transactions.
Transaction value
influences risk
assessment and thedecision-making
process.
Variance in fraud
risk due to different
regulatory
standards in EU andnon-EU countries.

34

2.B North America

2.C Asia-Pacific

2.D Middle East
and Africa

2.E Latin America
and Caribbean

2.F Emerging vs.
Developed
Markets
3. Sector-SpecificGroups

3.A Financial vs.
Non-Financial

3.B High-Risk vs.
Low-Risk
Industries
3.C Emerging vs.
EstablishedIndustries
3.D Regulated vs.
Less Regulated
Industries

Transactions in the
United States andCanada.

Unique financial
regulations andfraud patterns inNorth America.
Transactions in East Diverse economic
Asia, Southeast Asia systems andand Oceania.
regulatoryenvironments
impact fraud risk
differently in the
Asia-Pacific region.
Transactions in
The distinct
Middle East andeconomic andAfrican countries.
political landscapesin these regions
influence fraud risk.
Transactions in
Various levels ofSouth American
economic
countries and thedevelopment andCaribbean.
financial regulations
affect fraud
patterns.
Based on the
Emerging markets
economic
have different fraud
development status risks and patternsof the countries
than developed
involved.
markets.
Transactions
The financial sector
involving thehas various risk
financial and nonprofiles andfinancial sectors.
regulatory
requirements.
Based on the
Industries such asindustry's risk level: construction andenergy might havehigher risk profiles
than others.
Transactions in
Emerging industries
emerging andmay present
establisheddifferent financial
industries.
behaviors and risks.
Based on the level of Industries withregulatory oversight heavy regulatory
in the industry.
oversight may
exhibit different35

4. Transaction SizeGroups

5. Counterparty
Behavior Groups

Group transactions
by monetary value.

5.A High-Risk
Transactions
5.B Medium-Risk
Transactions
5.C Low-Risk
Transactions

Transactions withhigher potential for
financial loss, fraud,
or illegal activities.
Transactions withmoderate risk level
Regular small-scaletransactions alignwith known profiles
in low-riskcountries.

transaction
patterns.
Large transactions
might be treateddifferently by AI
models than smaller
ones.
Involving high-riskcountries or large
atypicaltransactions.
Somewhat unusual
but explainabletransactions.
Transactions are
considered safe orpose minimal risk.

Note: This table serves as the framework for conducting a comparative analysis of the FinFair AI Index
calculations. It aims to precisely calculate key metrics such as the Statistical Parity Difference (SPD), Disparity
Parity Ratio (DPR), and Predictive Parity (PP). By categorizing transactions into these groups, we can effectively
evaluate and compare the performance of AI models across various dimensions of financial activities. This
classification allows for a more detailed and accurate assessment of fairness in AI systems by providing a
structured approach to identifying potential disparities in treatment and outcomes across different transaction
types, geographic regions, sectors, and counterparty behaviors. Utilizing this table as a reference point,
researchers can rigorously analyze the fairness of AI models, ensuring that they equitably handle the complex
and diverse nature of financial transactions.

5. Data bias vs Model bias
Particularly, within the financial sector, a compelling argument emerges for prioritizing
model bias variables over data bias in the development of AI systems. This recommendation
is founded on the premise that the scope of measurement in AI development is confined tothe available data. Consequently, a strategic emphasis on model bias variables does notinherently amplify the risk of data bias. Rather, the incorporation of a diverse array of model
bias variables should be considered a fundamental prerequisite for the development of fair
AI systems.
This approach is underpinned by the understanding that the nature and quality of datacollection are inextricably linked to the specific business context of the financial entity inquestion. It necessitates a dynamic and long-term strategy for accruing high-quality data.
Furthermore, the focus on model bias aligns with a more human-in-the-loop approach. Unlike
data bias, which is predominantly influenced by the financial institution's business model

36

and the time-dependent nature of data acquisition and refinement, model bias is more
directly subject to the entity's control and intent.
This approach is particularly significant when considering the entry of new financial
institutions into the market. A requirement for extensive, diverse, and high-quality data as aprecondition for market entry could inadvertently establish prohibitive barriers for theseemerging entities. By focusing on model bias —while not undermining the importance ofdata bias — the financial sector can foster a fairer business environment. It enables newmarket entrants to progressively work towards de facto fairness, evolving through sustainedinteractions and gradual enhancement of their data pool.
Nevertheless, the significance and weighting of these parameters must be carefully
calibrated in accordance with the specific AI application—be it fraud detection, credit
lending, or other financial services. This nuanced approach ensures that the establishedfairness metrics are aptly tailored to the unique demands and challenges of each application.
The forthcoming sections of this research will delve into a comprehensive taxonomy,delineating a differentiated ratio and weighting scheme based on the AI application.

Weighting, justification and thresholds
Fairness Variables Weighting in AI Applications: Credit Lending vs. Fraud Detection
Variable

Definition

CreditLending
WeightingHigh

FNR (False
Negative Ratio)

Ratio of false
negatives toactual positive
cases.

FPR (False
Positive Ratio)

Ratio of false
Moderate
positives toactual negative
cases.

Fraud
Detection
Weighting
High

High

Justification
Crucial foravoiding
unfair loan
denials in
lending andfor effective
identificationof fraudulent
activities in
fraud
detection.
Less critical inlending but
vital in fraud
detection to
avoid
misclassifying
legitimate
activities.

37

SPD (Statistical
ParityDifference)

Balance of
positiveoutcomes
betweengroups.

High

Moderate

DR (Disparity
Ratio)

Ratio ofpositiveoutcomes
betweengroups.

Moderate

Low toModerate

PP (Predictive
Parity)Equal true
positive rates
across groups.

ModerateHigh

Shannon Index

MeasuresHighdiversity of thedataset.

Moderate

Fa (Awareness
Factor)Degree oforganizationalawareness of
AI biases.

ModerateHigh

Ensures nondiscriminatory
practices in
lending; focus
in fraud
detection ismore onaccuracy.
Important for
fairness inlending; lesscritical in
fraud
detection.
Ensures
accurate
identificationofcreditworthy
individuals in
lending;
critical for
equitable
accuracy in
fraud
detection.
Essential inlending for arepresentative
dataset;important in
fraud
detection for abalanced
approach.
High inlending due toits impact on
individuals;
moderate in
fraud
detection tobalanceawareness38

Fd
Degree ofHigh(Documentation documentation
Factor)of AI biases.

Moderatewith model
precision.
Vital inlending fortransparency;
important in
fraud
detection formodel
refinement
andcompliance.

Weighting :Low Weighting:Numerical Range: 0 – 0.33 (on a scale of 0 to 1)
Variables within this demarcated range are posited to exert a relatively marginal influence
within the overarching structure of the model. Their role, while not negligible, ischaracterized by a subsidiary impact on the decision-making algorithm. This classification is
integral to the hierarchical structuring of model variables, a practice that gains heightened
significance in complex, multi-variate AI systems, especially those deployed in our financial
contexts where numerous variables can potentially interact in a multifaceted manner.
Moderate Weighting:Numerical Range: 0.34 – 0.66 (on a scale of 0 to 1)
This bracket encapsulates variables that assert a substantive, albeit not dominant, influenceon model outcomes. Such variables are significant in shaping the predictive outcomes of themodel but are balanced against other variables to prevent any disproportionate skewing ofresults. This moderate classification serves to maintain an equilibrium within the model,
ensuring that the predictive capacity is not overly reliant on any singular variable. This
aspect is particularly pivotal in some cases, where the equitable representation of variables
is crucial to maintain the integrity and accuracy of the model.
High Weighting:Numerical Range: 0.67 - 1 (on a scale of 0 to 1)
Variables classified within this range are deemed to be of paramount importance. They are
the principal drivers of the model, critically influencing its output and decision-making
process. The attribution of high weighting to these variables underscores their pivotal rolein the model's functionality and the accuracy of its predictions. In the realm of financerelated AI applications, the implications of decisions driven by these variables are profound,necessitating meticulous validation and continuous scrutiny.
39Justifications for Variable Weightings:

FNR (False Negative Ratio)
Credit Lending: High FNR could systematically exclude creditworthy applicants, particularly
those from historically underserved backgrounds, perpetuating financial inequality andreinforcing historical biases.
Fraud Detection: High FNR can result in overlooking actual fraudulent activities,
compromising the integrity of financial systems and potentially leading to significant
financial losses and reputational risks.
FPR (False Positive Ratio)
Credit Lending: Moderately weighted, as false positives primarily lead to financial risks forthe institution but less direct social impact. However, over time, high FPR can erode the trustin AI systems.
Fraud Detection: High FPR can lead to unnecessary investigations, strain resources, andcause inconvenience to customers, affecting the user experience and potentially leading to aloss of customer trust.
SPD (Statistical Parity Difference)
Credit Lending: Ensures fair access to financial services across different demographic
groups, which is crucial for promoting financial inclusion and preventing systemic
discrimination.
Fraud Detection: Moderate emphasis on SPD as the focus shifts towards the precision of
fraud detection, though parity remains important to prevent systemic biases against certain
groups.
DR (Disparity Ratio)
Credit Lending: Ensures that the lending decisions are not disproportionately favoring or
disadvantaging any particular group, crucial for maintaining public trust and regulatorycompliance.
Fraud Detection: Lower emphasis as the primary goal is the accuracy and effectiveness offraud detection mechanisms.
PP (Predictive Parity)
Credit Lending: Ensuring consistency in the accuracy of predictions across different groups
mitigates the risk of biased lending decisions and promotes fairness.
Fraud Detection: High emphasis as it ensures that the system's predictive accuracy is
uniform across different groups, crucial for a fair and just financial security system.
Shannon Index (Diversity of Data)
Credit Lending: High diversity in training data is essential to capture the wide spectrum of
applicant profiles, reducing the risk of embedding historical biases into AI systems.
40

Fraud Detection: Moderately weighted, as while diverse data is important for comprehensive
fraud detection, the precision and adaptability of the model are more critical.
Fa (Awareness Factor)
Credit Lending: High awareness reflects an institution's commitment to identifying andmitigating potential biases in their AI models, crucial for ethical lending practices.
Fraud Detection: Moderately weighted as it's essential to understand potential biases, but
the primary focus is on the technical robustness and adaptability of fraud detection
algorithms.
Fd (Documentation Factor)
Credit Lending: Essential for maintaining transparency, accountability, and continuous
improvement in AI-driven decision-making processes.
Fraud Detection: Important for regulatory compliance and for ensuring that the model’sdevelopment and deployment are well-documented and can be audited for biases.
Fairness Scoring Threshold

Score Range
0-2

Category
Very Unfair

3-5

Unfair

6-7

Moderately Fair

8-9

Fair

10

Perfectly Fair

Description
Severe fairness issues,
clear biases, lack ofawareness or
documentation (lowhuman intervention).
Some awareness andefforts, but notable biases
and shortcomings.
Good awareness anddocumentation, room for
improvement.
Largely fair, minor biases,
strong commitment tofairness.
Exemplifies highest
standards of fairness,minimal to no biases.

41

5. Debate
Organic Solidarity towards de facto Fairness
In this research, we employed the Emile Durkheim’s paradigm to establish our FinFair AI
Index, aiming to transcend the conventional concept of mechanical solidarity, which is as theprevailing paradigm in the field of finance, particularly with regard to scoring in a generalcontext. Concerning fairness, a notable absence of a definitive framework remains.
This paradigm shift is particularly evident when considering the existence of scoring models
that are prevalent in the financial sector. These models are deployed to evaluate bank risks
across diverse regulatory domains irrespective of the specific compliance focus. However,the present situation seems to discourage proactive engagement as it does not inherently
foster a virtuous cycle conducive to diminishing systemic risk. To date, empirical evidence
remains elusive to demonstrate that models such as the Internal Ratings-Based (IRB)
approach effectively contribute to the mitigation of systemic risk.
The crux of the matter lies in the dual nature of these models, which are either overly
standardized or excessively individualized, primarily owing to the latitude provided for thecreation of bespoke internal models. This inherent rigidity precludes any meaningfulinterbank collaboration towards tangible reduction in systemic risk within the financial
sector.
Furthermore, empirical research convincingly illustrates that during times of financial
turmoil, the probability distribution transcends the normal curve, placing financial
instruments in the extremities of the tails. Consequently, even assets with ostensibly
disparate behavior dynamics under normal market conditions, such as equities and bonds,
tend to exhibit correlated behavior during crisis periods.
Considering these challenges, our Durkheimian-inspired approach might hold promise insteering the financial sector towards more cohesive solidarity, a more unified and structured
form of solidarity, particularly in the context of AI fairness scoring within the B2B landscape.
The foundation of this promise is rooted in the FinFair AI Index, which might offer astandardized yet personalized framework. This framework effectively bridges the gap
between regulatory or public sector initiatives and financial institutions.
Moreover, it facilitates the emergence of organic solidarity, paving the way for a moreprobable transition towards de facto fairness. This underscores the multifaceted nature of
our approach, as it proactively addresses model biases and their regulation. In this context,
regulatory authorities wield considerable influence on calibrating fairness scores. Thiscalibration process hinges on banks' commitment to fostering awareness, comprehensively
documenting biases, and accounting for data diversity through application of the Shannon
Index. In the B2B context, fairness is primarily a collective concern before becoming abilateral concern among banks, for instance, when utilizing AI models for fraud assessment.
42

Finally, the paradigm rooted in organic solidarity proves to be enticing, as the FinFair AI
Index is meticulously structured to promote incentive-driven collaboration, eschewing
competitive undertones. Significantly, the FinFair AI Index outcome serves as a discerning
tool to assess the fairness score of the AI systems employed by individual banks. Notably,
direct comparisons between such scores are infeasible, owing to the inherently tailorednature of the framework. Additionally, this paradigm exhibits an inherent incentive
structure by facilitating constructive alignment between a bank's commitment to fairness,
its control mechanisms addressing model bias and data bias, and overall performance.
Within this academic framework, an enhanced comprehension of one's model and datatranslates into a more nuanced understanding of market evolution and transformation,
thereby cultivating heightened awareness of counterparties. Ultimately, this enrichedknowledge base contributes to the optimization of performance within commercial
relationships.
This paradigm shift is not merely an academic exercise; it addresses the real costs faced byfinancial institutions owing to regulatory divergence. The findings reveal that regulatory
divergence incurs substantial financial costs, averaging between 5% to 10% of the annual
turnover for financial institutions6. These costs create a significant burden that reduces thepossibility of achieving higher performance, adding urgency to the need for innovative
approaches, such as the Durkheimian-inspired framework. It not only champions fairness,but also offers a practical and financially advantageous pathway for banks to navigate thechallenges of risk assessment and regulatory compliance, thereby potentially offsetting theweight of these incurred costs.

Operational risks for the FinFair AI Index Framework
Operational risks for the FinFair AI
Index Framework
Minor challenge

6

Description
Despite the framework's intention to
discourage direct score comparisons, apotential challenge arises from thefinancial institutions’ ability to assess theevolution of fairness scores over time.
However, given the sector's familiarity
with complex mathematical indices, any
disruption to the framework's efficiency isunlikely. This issue, while minor, warrants

IFAAC. (2018, February). Regulatory divergence: Costs, risks, impacts IFAC.
https://www.ifac.org/_flysystem/azure-private/publications/files/IFAC-OECD-RegulatoryDivergence.pdf. https://www.ifac.org/_flysystem/azure-private/publications/files/IFAC-OECDRegulatory-Divergence.pdf

43Moderate challenge

Major challenge

ongoing monitoring of the long-term
effects on the organic solidarity paradigm.
Determining a suitable regulatory
authority to oversee the framework is amoderate challenge. The choice between a
regional or global regulator and theestablishment of a dedicated international
agency requires careful consideration. Thissituation may necessitate a one-to-many
approach, obliging all financial entities toprovide their models and data for exclusive
analysis by a designated agency. Such a
decision ensures the accurate calibration of
the FinFair AI Index and the ongoing
monitoring of variable developments
within each bank. The regulatory oversightissue requires thoughtful deliberation toguarantee the effective and impartial
administration of the framework.
A substantial challenge arises fromdisparities among banks in terms of accessto AI resources and funding to addressmodel and data biases. Additionally, theShannon Index's reliance on data diversity
raises questions when banks must adapt
their datasets to accommodate evolving
business models. However, it is essential torecognize that these challenges may yield
benefits, particularly for banks withevolving business models, diversified
relationships, and distinct criteria. Overtime, they may accumulate a more diverse
dataset, potentially bolstering theirfairness scores in the long term, albeit notnecessarily in the short term. Addressing
this major concern requires addressing
resource disparities and potentially reevaluating the application of the Shannon
Index in varying business contexts within
the financial sector.

Fairness vs Performance
In the milieu of economic downturns, it is a well-documented phenomenon that average
performance across financial markets tends to wane, a trend often ascribed to endogenous
44

dysfunctions within the financial system itself. These dysfunctions typically arise fromflawed organizational structures or suboptimal interactions among the constituent entities.
This perspective aligns with the broader understanding that crises are frequently the resultof internal systemic issues rather than external shocks. Consequently, the integration ofenhanced fairness into AI systems is posited as a significant countermeasure against suchendogenous dysfunctions. By reducing information asymmetry and fostering anenvironment of heightened trust and transparency, particularly through the deployment offair AI systems, the financial sector can mitigate the adverse impacts of these crises.
Empirical research corroborates that the diminution of information asymmetry leads toenhanced business opportunities7, largely due to the resultant bolstered trust among market
participants. This diminution is pivotal in facilitating a more discerning and fair treatment ofdiverse groups, thereby engendering a heightened level of trust among stakeholders.
Contemporary research substantiates the assertion that the amelioration of information
asymmetry bears a direct corollary to an amplification of business opportunities,
fundamentally attributed to the bolstered trust resonating among market participants8.
In addressing the integration of fairness in AI systems within the B2B financial sector, it
becomes evident that this endeavor does not contravene the objectives of market
performance. Instead, it serves as a complementary and essential aspect of the financial
ecosystem. The pursuit of enhanced fairness in AI is inexorably linked to one of thequintessential challenges in finance: the diminution of information asymmetry.
The deployment of AI systems characterized by heightened fairness is instrumental indisseminating more comprehensive and transparent information about the profiles offinancial counterparties. This enhanced clarity plays a pivotal role in mitigating the
traditionally pervasive issue of information asymmetry in financial markets. By yielding amore nuanced understanding of the entities engaged in financial transactions, these
sophisticated AI systems catalyze informed, fair decision-making.
Consequently, an increase in trust and confidence among market participants emerges,
fostering a milieu conducive to improved market stability and performance. This scenario
underscores the strategic significance of fairness in AI within the financial sector,
transcending beyond mere compliance imperatives.
Thus, the integration of fairness into AI systems within the financial sector should beperceived not as a divergent goal but as an integral component that enhances market
performance.

7

Gudelytė, L. (2015). On the impact of information asymmetry on evaluation and risk of cluster performance.
Socialinės Technologijos, 5(01), 32-43.
8
Hicks, J. R. (1936). Keynes’ Theory of Employment, Interest and Money. The Economic Journal, 46(182), 238-253.

45

6. Conclusion
This study has explored the transition from mechanical to organic solidarity in the B2B
financial sector. By developing the FinFair AI Index framework, we have introduced acomprehensive tool aiming to score fairness of AI. Our research underscores the importanceof collaboration between public authorities and financial institutions in ensuring fair AI
systems.
The FinFair AI Index framework emerges as a pivotal innovation in advancing bothprocedural fairness and de facto fairness in the financial sector, by implementing a noncompetitive and individual fairness scoring system ranging from 0 to 10. This frameworktranscends traditional approaches by not only ensuring compliance with regulatory
standards but also by fostering an environment of organic solidarity. This concept ofsolidarity signifies a deeper integration and cooperation among financial institutions. The
FinFair AI Index extends beyond the confines of a simple regulatory tool as it serves as acatalyst for a paradigmatic shift, wherein the interactions between banks become crucial forthe cultivation of fairness.
Our framework, which accounts for both model biases and data biases, represents asignificant step toward addressing the complexities associated with AI fairness. The
inclusion of parameters such as the Disparity Ratio (DR) and Predictive Parity (PP) in our
analysis has allowed for a more detailed and sophisticated understanding of how fairness
can be quantified and improved in financial decision-making systems.
Moreover, our study highlights the significance of continuous adaptation and improvementin AI models, reinforcing the need for ongoing AI development against biases. By
documenting and actively addressing these biases, financial institutions can not only comply
with standards but also enhance their individual development towards fair AI systems andperformance.
In conclusion, this research contributes to the growing discourse on AI and fairness infinance, providing valuable insights and tools for financial institutions to navigate andimprove the fairness of their AI systems. It is our hope that the FinFair AI Index frameworkand our findings will inspire further research and action in the pursuit of a fairer financial
system in the B2B context.

46

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