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 […]
FDCPA Section 807 and AI: When Generated Messages Cross the False Representation Line
A Scripted Letter Can Be Wrong. A Generative System Can Be Wrong Differently, at Scale. TL;DR A collections letter written by a compliance team and approved once can be wrong, but it’s wrong in a way someone already reviewed and signed off on. A generative AI system composing a new message for every borrower, every […]
BNPL Collections in 2026: What Applies Now That the CFPB’s Credit-Card Classification Was Withdrawn
Building Toward a Rule the Bureau Itself Withdrew Solves Nothing TL;DR If your BNPL collections build is still oriented around satisfying the CFPB’s card-issuer classification, it’s worth pausing to check the date. The Bureau withdrew that rule in May 2025 and confirmed the following month it has no intention of bringing it back in that […]
Hardship Workflow Automation: How AI Routes Distressed Borrowers to the Right Programme
Two Borrowers in Identical Distress Shouldn’t Get Different Outcomes Because They Reached Different Agents TL;DR A borrower calls in genuinely struggling. Depending on which agent picks up, on their training, their caseload that day, their read of an ambiguous situation, that borrower gets routed to a forbearance conversation, a payment plan pitch, or nothing more […]
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 […]
