Why Explainable AI in Banking and Finance Is Critical for Compliance

Human oversight of AI algorithms at work is essential. In the banking and finance sector, explainable AI (XAI) is a key component of AI compliance for several interconnected reasons, primarily centered around abiding by regulatory demands, managing risk, ensuring fairness, and maintaining stakeholder trust.

From the simplest, most linear AI decision trees to large LLMs and custom enterprise models, any type of AI must allow human-led teams to assess the safety and accuracy of the AI reasoning that takes place behind the scenes and scrutinize its process of decision-making at all times, before any incident arises that may become a liability under regulatory scrutiny.

The banking and finance sector is increasingly relying on AI and ML to enhance operations and services. Applications span credit scoring and risk assessment, fraud detection, anti-money laundering (AML) compliance, comprehensive risk management, algorithmic trading, customer service, and portfolio management, offering benefits like better predictions and task automation at reduced costs.

However, this technological advancement introduces a significant challenge: the inherent complexity and opacity of many sophisticated AI models, particularly those based on deep learning or intricate algorithms. This lack of transparency hinders comprehension, erodes trust, and complicates accountability, creating substantial risks within a sector defined by stringent regulation and the need for high levels of confidence. Explainable AI aims to characterize key aspects of AI models, including their accuracy, fairness, transparency, and the outcomes they produce in decision-making processes.

White Box vs Black Box AI Models: An Analogy from Psychology

The more advanced AI models we previously mentioned often function as “black boxes”, where the internal logic connecting inputs to outputs is not readily understandable by humans, even by their developers themselves. Just for fun, let’s make a parallel between AI and the black box approach in human psychology.

At some point in 20th century psychology, a certain paradigm called behaviorism became very popular. This approach pictured people’s minds as mere black boxes, their processes hidden and not interesting enough to bring to light and explain. The sole focus point was on behavior, as the result of learning through conditioning: provide a stimulus, offer a reward, obtain a desired response, in short. Whether it was about a kid doing schoolwork, a consumer buying a product, or a dachshund learning to play fetch, the process was basically similar. (Of course, at present we know – and care about – a lot more of the delicate interplay of variables that shape human thinking.)

Behavior is observable and measurable, while thinking is not readily so – that’s a fact we can’t deny, in humans and AI systems. Because of the lack of a “scientific” way to explain and measure thought processes, behaviorists resolved not to analyze the mind anymore. Thus, the only part that concerned these folks was how to observe, measure, and influence people’s visible behavior – the latter by introducing “an input” in the black box, such as a stimulus and a command, followed by a course of rewards or punishments, to get that motivational ball rolling. All of this was meant to determine people to lean towards a desirable path through conditioning, thus shaping the “output”. Does it sound like AI yet?

All was well, up to a point – the trouble with the “black box”, as with all reductionist perspectives that don’t attempt to understand the underlying phenomenon and its mechanisms or determinants, is that they cannot explain outliers and erratic results which don’t fall within the expected output structure designed by their parent theories. Behaviorist scientists were baffled as to why some results observed in their test populations were skewed and out of the norm, why some experiments simply failed or yielded inconclusive results, or why others led to spectacularly different outcomes than the ones first hypothesized. For AI, we call it model drift.

As an effect of too many unexplained phenomena within their reach, as well as of other limitations and critiques, that current in psychology has now become obsolete. It has moved away to give room to more scientifically sound and empirically promising pathways, such as cognitive psychology (which has also provided research that explores the structure and function of human neural pathways and cognitive models that fueled the development of AI).

Back to AI in banking and finance, the explainable AI field overlaps significantly with concepts like “interpretable AI” and “explainable machine learning” (XML). While definitions vary, “interpretability” often refers to models whose structure and reasoning output are inherently understandable (e.g., simple linear models or decision trees), sometimes termed “white-box” models. “Explainability,” conversely, frequently involves applying post-hoc techniques to render the decisions of more complex, opaque (“black box”) models understandable after they have been trained.

Simpler models are easier to understand but may lack the predictive power of complex black boxes, especially on intricate datasets. Post-hoc explainability methods like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) attempt to provide the best of both worlds – leveraging high-performing complex models while adding a layer of explanation.

SEE ALSO: 3 Hidden Risks of AI for Banking and Insurance Companies

Core Principles of Explainable AI in Banking and Finance

The development and application of XAI are guided by several core principles, often drawing from frameworks like that proposed by the US National Institute of Standards and Technology (NIST). These principles ensure that explanations for the AI output are not only generated but are also useful and reliable:

• Provision of explanation: AI systems must justify their outcomes with evidence or reasoning.
• Meaningfulness: Explanations must be understandable and useful for the intended audience.
• Explanation accuracy: Explanations must truthfully reflect the AI’s underlying process.
• Knowledge limits: AI should recognize and indicate when operating outside its validated limits.
• Transparency & interpretability: AI should provide insight into its workings and present decisions understandably.

AI in Banking and Finance: The Regulatory Gauntlet

European Union

The EU has taken a leading regulatory role with the EU AI Act, which can also apply to institutions outside the EU if their AI systems affect or are used within the EU.

Crucially for the financial sector, the AI Act designates certain applications as High-Risk, including AI systems used for:

• Evaluating the creditworthiness or establishing a credit score
• Risk assessment and pricing for life and health insurance.

Providers and deployers of these High-Risk AI systems face stringent obligations throughout the system’s lifecycle, including transparency and provision of information to users, as well as human oversight.

Non-compliance with the AI Act carries significant penalties, with fines potentially reaching up to €35 million or 7% of the company’s total worldwide annual turnover for the preceding financial year, whichever is higher, for the most serious infringements, like using prohibited AI practices or violating high-risk system requirements.

United States

In the US, key guidance for AI in banking includes SR Letter 11-7, which outlines model risk management frameworks, and Fair Lending Laws like ECOA, which prohibit discriminatory credit practices based on protected characteristics, directly impacting AI model fairness.

While legal details vary globally, there’s a clear convergence in regulatory expectations demanding transparency, rigorous validation, strong governance, demonstrable fairness, and proactive risk management for AI systems. Regulations are shifting towards being proactive, mandating assessments and governance before AI deployment, rather than just reacting to failures. This necessitates that institutions enhance their existing frameworks by incorporating AI-specific policies, expertise, validation methods, and importantly, Responsible AI platforms to ensure compliance and ethical implementation.

Why XAI: From Improved Decision-Making to Winning Stakeholder Trust

Here’s a breakdown of why explainable AI for banking and finance is so important.

Regulatory Compliance

As previously stated, legal mandates worldwide increasingly require transparency and the ability to explain automated decisions, especially those significantly impacting consumers (e.g., loan approvals, credit scoring). XAI provides the mechanisms to meet these requirements.

Auditability is another requirement. Regulators and internal audit teams need to be able to scrutinize AI models used in finance. XAI makes models interpretable, allowing for effective auditing and proof of compliance. Without explainability, validating a model’s fairness and adherence to regulations during an audit becomes extremely difficult.

Better Decision-Making

XAI, or Explainable AI, in banking and finance significantly enhances decision-making by providing transparency and interpretability into complex AI models. This allows financial institutions to understand and validate their reasoning behind critical decisions, such as loan approvals, fraud detection, and investment recommendations.

In the realm of loan approvals, XAI provides crucial insights into why an AI system approved or rejected an application. Instead of a black-box decision, lenders can understand the specific factors the model considered, such as credit history, income, debt-to-income ratio, and other relevant variables. In fraud detection, XAI moves beyond simply flagging suspicious transactions to explaining why they are considered suspicious. For investment recommendations generated by AI, XAI provides users with the rationale behind the suggested portfolios or investment choices, which leads to increased user trust and higher adoption of the financial instrument.

Fairness and Bias Mitigation

A critical concern surrounding the use of AI in finance is the potential for algorithmic bias, which can lead to unfair or discriminatory outcomes, particularly against legally protected groups. Bias can creep into models through unrepresentative or historically biased training data or through the design choices made during model development.

Explainable AI techniques provide indispensable tools for both detecting and mitigating algorithmic bias, enabling a shift towards more proactive fairness assessments during the model lifecycle rather than relying solely on reactive checks after deployment. Traditional bias testing often involves analyzing model outcomes across different demographic groups once a model is built. XAI, however, allows developers and validators to probe the internal logic of the model before it impacts customers.

Stakeholder Trust

When decisions like loan denials or flagged transactions occur, customers expect explanations. XAI enables banks to provide meaningful reasons, fostering trust, improving customer experience, and potentially strengthening relationships even when delivering unfavorable news.

Internal trust and collaboration is also a point to make. XAI bridges the gap between technical teams (data scientists) and non-technical stakeholders (compliance officers, risk managers, executives). When all parties can understand how an AI model works and why it produces certain outputs, it builds internal confidence and facilitates better decision-making and governance.

SEE ALSO: AI in Finance: The Rise and Risks of AI Washing

XAI in Action: Key Use Cases for Banking AI Compliance

The necessity for explainable AI in banking and finance is not merely theoretical; it manifests directly in the practical application of AI across various high-stakes functions. The specific AI compliance drivers can vary depending on the use case, and they demand tailored strategies rather than a one-size-fits-all approach.

Credit Risk Assessment and Scoring

• AI use: AI models, including complex ML algorithms, analyze extensive datasets encompassing traditional financial data, alternative data, and behavioral patterns to predict borrower creditworthiness and assign credit scores.
• Compliance need: This is a primary area of regulatory focus. Credit scoring systems are explicitly designated as high-risk under the EU AI Act, and fall under the risk management guidance of US Fair Lending laws (like ECOA) and others.
• XAI role: For credit scoring compliance, XAI is vital as it explains individual loan decisions (using methods like LIME and SHAP to identify key factors), enabling compliant adverse action notices. It also plays a critical role in detecting and mitigating bias, ensuring fair decisions, and supports the rigorous model validation required by regulations like SR 11-7.

Anti-Money Laundering (AML) and Fraud Detection

• AI use: AI and ML are increasingly used to enhance AML and fraud detection capabilities. They analyze vast transaction volumes to identify complex, subtle patterns indicative of money laundering or fraud that traditional rule-based systems often miss.
• Compliance need: Financial institutions must comply with stringent AML regulations set by bodies like FATF, FinCEN, and under EU AMLDs.
• XAI role: In AML, explainability is crucial for justifying AI alerts by detailing the specific factors triggering suspicion, building trust with regulators and auditors. It also significantly improves operational efficiency by enabling analysts to quickly understand alerts, investigate genuine risks, and reduce false positives.

Algorithmic Trading

• AI use: AI is employed to develop and execute high-frequency trading strategies, optimize trade execution to minimize market impact, manage portfolio risk in real-time, and identify complex market patterns.
• Compliance need: Algorithmic trading is subject to market integrity rules aimed at preventing manipulation and ensuring fair market operation.
• XAI role: XAI improves the transparency of complex trading algorithms, enabling firms to understand the reasoning behind decisions and ensure regulatory and ethical compliance.

Other Applications

The need for explainability extends to other financial AI applications as well:

• Customer churn prediction: Understanding why models predict a customer might leave helps ensure that retention strategies are fair and not based on discriminatory factors.
• Portfolio management: Explaining AI-driven risk assessments and investment recommendations is crucial for portfolio managers and clients.
• Insurance risk assessment and pricing: Similar to credit scoring, AI used for pricing life and health insurance is deemed high-risk under the EU AI Act, necessitating explainability for fairness and compliance.

In essence, the complex, high-stakes nature of financial services, combined with stringent regulatory oversight and the need for customer trust, makes the transparency offered by explainable AI not just beneficial but increasingly essential for compliance. Relying on “black-box” models poses significant regulatory, reputational, and financial risks that financial institutions cannot afford to ignore.

An integrated AI governance platform like ours can provide the necessary explainability, governance, and risk management capabilities tailored for your institution. Schedule a personalized demo today to explore how our solutions help financial organizations like yours build trust and compliance into their AI initiatives.

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Related topics: Banking & Investment