AI Governance and Risk Management in Financial Institutions at Kotak Mahindra Bank

Abstract

This study, titled "AI Governance and Risk Management in Financial Institutions at Kotak Mahindra Bank," evaluates governance framework pillar allocations, model audit resolution timelines, compliance cost savings, and financial feasibility of enterprise AI governance portals in private sector banking. Commercial banks face increasing regulatory scrutiny over algorithmic decisioning, with model explainability representing 40% and bias mitigation accounting for 28% of governance priorities. A five-year project lifecycle (2021-2025) of an AI governance and risk management portal at Kotak Mahindra Bank is evaluated using capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative analysis reveals that deploying a real-time AI governance portal reduces model risk audit resolution times to 3.5 days compared to 45.0 days under legacy manual risk committee audits. Scaling AI audits to 850 models generates 310 Crores in annual compliance cost savings, expanding governance compliance rates to 98.5% and compressing model bias incident rates to 0.6% by 2025. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, far exceeding the 10% discount hurdle rate. The study concludes that investing in automated AI governance is highly viable, protecting institutional reputation and regulatory compliance. Keywords: AI Governance, Risk Management, Model Explainability, Bias Mitigation, Kotak Mahindra Bank, Regulatory Compliance, Capital Budgeting, Financial Feasibility.