Explainable Artificial Intelligence for the BFSI Sector: Integrated Governance Framework for AI and Decision Transparency

The rapid evolution of AI and Generative AI (GenAI) presents both opportunities and challenges for the Banking, Financial Services, and Insurance (BFSI) sector. While AI enables unprecedented efficiency, predictive accuracy, and operational scalability, it also introduces significant risks related to transparency, fairness, compliance, and trust. These risks underscore the need for Explainable Artificial Intelligence (XAI) frameworks that extend beyond technical model interpretability to address governance, ethics, traceability, and human oversight. This white paper contributes an original, empirically validated framework — the Explainable AI Decision Transparency Framework (EADTF) — specifically designed to support BFSI institutions adopting AI and GenAI systems. The framework synthesizes insights from academic literature, global regulations (DPDPA, RBI MGCG, GDPR, EU AI Act), and industry best practice into a unified, actionable structure. Organized into five mutually reinforcing layers — Data Transparency, Model Interpretability, Decision Transparency, Governance & Oversight, and Ethical & Responsible AI — EADTF offers a comprehensive lifecycle approach that operationalizes responsible AI adoption. The empirical validation using a dataset of 340 BFSI professionals significantly strengthens the model’s credibility. Structural Equation Modelling (SEM) demonstrated strong measurement reliability (Cronbach’s α = 0.67–0.82), valid constructs (AVE and composite reliability within acceptable thresholds), and robust predictive power (Overall Value R² = 0.4957). Key findings revealed that transparency significantly improves trust (β = 0.2485), customer satisfaction (β = 0.2516), and perceived overall value. Regulatory compliance, while not a strong direct predictor, has powerful indirect effects (total β = 0.3441), strengthening model transparency, accuracy, and trust. These results demonstrate that responsible AI adoption in BFSI is inherently multi-dimensional and cannot be achieved through technical interventions alone. The EADTF framework fills the gap identified in XAI literature by integrating ML and GenAI explainability in a manner aligned with governance and regulatory expectations. Traditional interpretability tools — such as SHAP, LIME, and partial dependence plots — offer technical clarity but lack applicability for GenAI models that produce non-deterministic, context-dependent outputs. EADTF extends the scope of explainability to include GenAI-specific requirements such as hallucination monitoring, retrieval attribution, prompt governance, and output scoring — providing a governance-ready model for next-generation AI systems. The 12-month implementation roadmap presented in this paper offers BFSI institutions a structured plan for adopting EADTF across foundational governance (0–3 months), infrastructure and transparency enablement (3–6 months), business integration and oversight (6–9 months), and enterprise-scale deployment with continuous improvement (9–12 months). This practical roadmap ensures the framework is not merely conceptual but is directly applicable to real-world BFSI operations.

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