Every AI Operating System for Banking Needs a Governed Intelligence Layer
In my previous article, I argued that the next competitive advantage in banking will not come from artificial intelligence alone. It will come from an institution’s ability to transform raw data into trusted, reusable intelligence. Data records what happened. Intelligence explains what it means. That distinction is becoming increasingly important. Over the past few months, […]
Credit Card Collections AI: How Revolving Behaviour Changes the Propensity Model
Your Installment Model Isn’t Underperforming on Cards. It’s Answering a Different Question. TL;DR A collections model that performs well on personal loans and auto loans, then quietly underperforms the moment it’s pointed at a credit card portfolio, isn’t a model that needs more data. It’s a model answering a question revolving credit doesn’t ask. Why […]
RBI MRM Campaign | AI Model Bias, Drift, and Ongoing Monitoring
Your Model Passed Validation. Now It Is On Its Own. That Is the Problem. TL;DR Your credit scoring model passed validation 18 months ago. Since then, it has processed tens of thousands of applications. The macro environment has shifted. New borrower segments have entered your product mix. Several of the input features your model was […]
Your Vendor Validated The Model. RBI Says That Doesn’t Count
TL;DR Para 45 of RBI’s draft guidance on Model Risk Management contains one sentence that most vendor relationships in Indian banking are not built around. “An RE acquiring, using or relying upon third-party models at any stage of the model lifecycle is accountable for its outcomes.” That sentence is complete as written. The accountability does […]
Your Institution Probably Has 10x More Models Than You Think. Here Is How RBI Counts Them
TL;DR Think of the last three quantitative decisions your institution made. A lending rate was set. A collection account was routed to an agent. A loan application was declined. For each one, ask: did a tool take an input, apply some logic to it, and produce an output that drove that decision? If the answer […]
RBI Scale Based Regulation for Upper Layer NBFCs: AI Collections Governance Requirements
TL;DR RBI Scale Based Regulation and AI Collections Governance for Upper Layer NBFCs The intersection of RBI scale based regulation for Upper Layer NBFCs and AI collections governance has become the most scrutinized compliance domain for India’s largest non-bank lenders. RBI’s Scale Based Regulation for NBFCs, issued in October 2021, operationalized through the Upper Layer […]
SR 11-7 Model Inventory Requirements for Collections AI: What Counts as a Model in 2026
TL;DR SR 11-7 Model Inventory Requirements for Collections AI: What Banks Must Know in 2026 Most model risk management teams at US banks built their collections governance frameworks years before AI entered the collections workflow. That gap is now visible in examination findings. SR 11-7: Guidance on Model Risk Management, issued jointly by the Federal […]
SHAP Explainability for RBI Examinations: Account-Level Reasoning in NBFC Collections AI
TL;DR An RBI examiner asks for the reasoning behind account 847291’s propensity score during a model validation review. The NBFC’s collections head pulls up the dashboard. It shows a 0.73 score. There is no per-account explanation available, only a portfolio-level feature importance chart. This gap in SHAP explainability for RBI collections model examinations carries a […]
MLOps for Indian Bank and NBFC Collections Models: Retraining Governance and RBI MRM Compliance
TL;DR A ₹7,200 crore NBFC deployed its AI collections model 14 months ago. The Gini has drifted from 0.74 to 0.61. No drift monitoring system was running. No retraining was triggered. No RBI MRM documentation was updated. The next examination is in 6 weeks. This scenario captures the core operational risk facing MLOps for Indian […]
AI Self-Learning Models for NBFC Collections: How the Technology Works
TL;DR A propensity model deployed on a personal loan portfolio in January was trained on 18 months of historical payment data. At deployment, its scores were accurate. Recovery rates improved through the first quarter. By August, the picture has changed. Payment rates in the mid-propensity band have dropped. The model continues to route accounts to […]
