Explainable AI - AI for Digital Finance 2026

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Overview

Explainable AI (XAI) addresses the critical need for transparency in AI-driven financial decisions. As machine learning models become more complex, regulators and stakeholders increasingly demand clear explanations for credit decisions, risk assessments, and investment recommendations. This track focuses on methods to make black-box models interpretable while maintaining predictive performance. We explore both post-hoc explanation techniques and inherently interpretable models suitable for regulated financial environments where accountability and auditability are paramount.

Key Research Questions

How can SHAP and LIME be adapted for complex financial time series models?
What level of explainability satisfies regulatory requirements (ECB, BaFin, FINMA)?
How do we balance model complexity with interpretability in credit scoring?
Can attention mechanisms provide meaningful explanations for financial predictions?
What are best practices for communicating AI explanations to non-technical stakeholders?

Methodologies

SHAP (SHapley Additive exPlanations) values
LIME (Local Interpretable Model-agnostic Explanations)
Attention visualization for transformer models
Counterfactual explanations
Inherently interpretable models (GAMs, rule-based systems)

Industry Applications

Credit scoring model explanations for rejected applicants
Risk model audit trails for regulatory compliance
Investment recommendation justifications
Fraud detection alert explanations
Algorithmic trading decision logs

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