Personal Loan Collections in India: AI Behavioural Signals for Unsecured Recovery
Unsecured Delinquency Is Rising Even as the Overall Loan Book Looks Healthier Than Ever TL;DR The aggregate Indian banking system NPA story is a genuinely positive one right now, system-wide gross NPAs sit at multi-decade lows. But that headline masks a specific, well-documented divergence: unsecured personal loans are moving in the opposite direction, with delinquencies […]
NACH Mandate Failure Patterns: What Lenders Must Do When Auto-Debit Recovery Fails
A NACH Failure Isn’t a Payments Glitch. It’s a Collections Trigger Most Workflows Miss. TL;DR Every NBFC processing loans through NACH or ECS mandates has a queue of failed auto-debits sitting somewhere in the operational pipeline, usually being retried on a schedule with no real differentiation between a borrower whose payment failed once because of […]
MSME Loan Collections: How AI Uses Informal Income Signals When Traditional Scoring Fails
The Headline Number Says MSME Lending Is Healthier Than Ever. The RBI’s Own Report Says Look Closer. TL;DR The system-wide MSME story is genuinely a good one, and it’s worth stating plainly rather than manufacturing a crisis that doesn’t exist. Gross NPAs in the MSME book have fallen sharply over the past several years, and […]
FCRA and AI Collections: Bureau Data Obligations That Go Beyond Origination
Clean FCRA Documentation on Underwriting Doesn’t Cover What Collections Is Doing With the Same Bureau Data TL;DR A bank can have a clean, well-documented FCRA compliance program for its underwriting models, permissible purpose logic, dispute workflows, furnishing controls, all built and reviewed. Ask the same institution to produce equivalent documentation for how its collections AI […]
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 […]
Mortgage Collections and Loss Mitigation AI: Meeting CFPB Servicing Rules Without Manual Intervention
An AI System That Contacts a Borrower Mid-Review Isn’t Efficient. It’s a Violation. TL;DR A mortgage servicer’s AI collections system flags a delinquent account for outreach. The borrower submitted a loss mitigation application eleven days earlier. It’s still under review. The system doesn’t know that, because nobody wired loss mitigation status into the contact decision. […]
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 […]
NCA Section 86 and AI Collections: Real-Time Debt Review Integration for SA Credit Providers
TL;DR NCA Section 86, AI Collections, and the Case for Real-Time Debt Review Integration South African credit providers running AI-driven collections workflows face a compliance exposure that most batch-based systems cannot close: the gap between when a borrower applies for debt review and when the collections platform actually knows about it. NCA Section 86, as […]
