Explainable AI in Finance: A Strategic, Compliance‑First Playbook

Artificial Intelligence (AI) is reshaping finance. Yet its opaque “black boxes” raise serious concerns in high‑stakes domains like credit, trading, fraud detection, and risk compliance. Explainable AI (XAI), also called interpretable or transparent AI, promises to unlock trust, performance, and regulatory alignment when implemented correctly.

Why XAI Matters to Finance – Risk, Regulation, and Reputation

Regulatory Imperatives

Under the EU AI Act (Regulation 2024/1689), AI systems used for credit scoring, risk assessment, and automated decision-making are designated high-risk, triggering requirements for transparency, human oversight, risk documentation, and explanation of decisions (EU Parliament & Council, 2024; KPMG, 2024). Organizations deploying such systems must provide technical documentation, model risk assessments, and clear explanation capabilities, or face penalties up to €35 million or 7% of global revenue (KPMG, 2024; Lucinity, 2025).

In the United States, although no standalone AI law exists, SR 11‑7 remains the supervisory standard for model risk governance within banking. Its validation, monitoring, and documentation mandates now extend to AI and machine learning systems (Federal Reserve & OCC, 2011; ValidMind, 2024). Firms are expected to integrate AI-native concerns like bias, explainability, and concept drift into their SR 11‑7 frameworks (ValidMind, 2024; Coforge, 2023).

Globally, regulators—including in the UK—are advancing explainability requirements, signaling expectations that advanced finance AI models must be understandable, auditable, and socially accountable (Nannini et al., 2023).

Business Benefits Beyond Compliance

XAI delivers strategic advantages:

• Faster model validation and governance, easing auditability and reducing internal friction.
• Improved consumer trust, for example, through transparent rationale in loan decisions.
• Reduced bias, as explainability surfaces potential discrimination in decision logic.
• Legal defensibility, meeting both SR 11‑7 prescriptive requirements and EU AI Act obligations (Lumenova, 2025; Reuters, 2024).

What the Literature Tells Us: A 2024 Systematic Review

Černevičienė and Kabašinskas (2024) conducted a systematic literature review of 138 peer‑reviewed studies between 2005 and 2022, surveying how XAI is used in finance. Key findings:

• Main use cases include credit management, fraud detection, and stock/forecast prediction (Černevičienė & Kabašinskas, 2024).
• Preferred tools: LIME and SHAP dominate alongside feature‑importance and rule‑based methods, with hybrid multi‑method frameworks growing in popularity.
• Deficits and challenges: lack of standard evaluation metrics, insufficient user‑targeted explanations (e.g. for auditors vs. consumers), and under‑explored models outside credit scoring contexts (Černevičienė & Kabašinskas, 2024).

These findings highlight a field with rapid adoption but meaningful gaps—especially around governance, explainability quality, and adversarial robustness.

XAI Tools & Techniques in High‑Value Use Cases

Here’s how XAI is typically applied across finance domains:

• Credit scoring & risk: Gradient‑boosting models (e.g. LightGBM) paired with SHAP for feature attribution; counterfactual explanations offer “what‑if” insights. Interpretability-by-design models like GAMs and monotonic GBMs are used when feasible.
• Fraud detection & AML: Tree ensembles with SHAP/LIME supplemented by rule extraction or case-based prototypes to explain alerts to compliance teams.
• Portfolio optimization/trading: Sparse regressions or hybrid neural nets with local SHAP explanations or GAM surrogate models provide transparency into factor allocations.
• Time-series forecasting: Attention mechanisms or saliency maps combined with SHAP‑style attribution to justify automated trading decisions (Černevičienė & Kabašinskas, 2024).

Governance Frameworks for Trustworthy XAI

Successfully scaling XAI in finance depends on measurement and governance frameworks that map to both compliance and business assurance:

• SR 11‑7 mandates robust model risk management, now explicitly including AI explainability, fairness, and monitoring (Federal Reserve & OCC, 2011; ValidMind, 2024; Coforge, 2023).
• NIST AI Risk Management Framework (AI RMF) embeds transparency and explainability within four functions—Govern, Map, Measure, Manage. Firms can align internal controls to this structure to satisfy both U.S. and EU expectations.
• Emerging metrics like SAFE (Sustainability, Accuracy, Fairness, Explainability) and KAIRI (Key AI Risk Indicators) are gaining traction as operational tools to quantify XAI trustworthiness (Černevičienė & Kabašinskas, 2024; Lumenova, 2025).

By combining SR 11‑7 risk controls with AI-native frameworks like NIST RMF and SAFE/KAIRI, institutions can build explainability into their AI governance lifecycle.

Security Considerations and Adversarial Risks

XAI methods introduce novel attack vectors: adversaries might reverse-engineer inputs to generate favorable explanations, leak sensitive data, or game predictive outputs (Chen & Storchan, 2021). Mitigations include:

• Rate-limiting and sanitizing explanation APIs
• Logging and red‑teaming explanation outputs
• Differential privacy or concealing sensitive features
• Human‑in‑the‑loop review of high-risk explanations

DARPA’s XAI initiative reinforces that explanations must be meaningful and helpful to users—not just mathematically precise (Pavlidis, 2025).

Toward an Audit‑Ready XAI Deployment

A practical deployment road‑map:

1. Classify AI applications using EU AI Act risk tiers or internal inventory frameworks.
2. Model strategy:
   • Prefer explainable-by-design models when performance is comparable.
   • Otherwise, combine black‑box models with post-hoc tools and robustness tests.
3. Define audience artifacts: executives, validators, customers—design tailored explanations (Černevičienė & Kabašinskas, 2024).
4. Embed metrics: map XAI outputs to SAFE, KAIRI, SR 11‑7 & NIST control frameworks.
5. Document: produce technical documentation, model cards, and adverse action notices with counterfactuals.
6. Monitor: track concept drift, explanation stability, fairness trends, challenger model variance.
7. Secure: control explanation access, detect manipulation, enforce privacy.

Conclusion

Implementing XAI in finance isn’t a nice-to-have—it’s mandatory for regulatory compliance (especially under the EU AI Act and SR 11‑7), trust-building, and effective governance. Research shows that techniques like SHAP and LIME are widely used, but often without standardized metrics, audience‑aware design, or security thinking. By weaving explainability into model risk management frameworks, applying emerging metrics like SAFE and KAIRI, and balancing interpretability with performance, financial institutions can deliver transparent, fair, and auditable AI systems.

References

Černevičienė, J., & Kabašinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: a systematic literature review. Artificial Intelligence Review, 57(216). https://doi.org/10.1007/s10462-024-10854-8

Federal Reserve & OCC. (2011). Supervisory Letter SR 11‑7 on guidance on Model Risk Management. https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm

KPMG. (2024). Setting the ground rules: the EU AI Act. Online article.

Lucinity. (2025, April). AI Regulations in Financial Compliance: A Comparison of AI Regulations by Region – The EU AI Act vs. U.S. Regulatory Guidance. Retrieved from Lucinity blog.

Lumenova. (2025, May). Why Explainable AI in Banking and Finance Is Critical for Compliance. Retrieved from Lumenova.ai blog.

Nannini, L., Balayn, A., & Smith, A. L. (2023). Explainability in AI policies: a critical review of communications, reports, regulations, and standards in the EU, US, and UK. arXiv.

Pavlidis, G. (2025). Unlocking the Black Box: Analysing the EU Artificial Intelligence Act’s Framework for Explainability in AI. arXiv.

Černevičienė, J., & Kabašinskas, A. (2024). [Same as above]

Coforge. (2023). SR11‑7: A comprehensive guide to AI adoption and model risk management in banks.

ValidMind. (2024). AI in Model Risk Management: A Guide for Financial Services.