frenzofinserv

  Every lender that grows past a certain portfolio size hits the same wall. The collections team that handled two thousand overdue accounts with phone calls and a shared spreadsheet is now staring at twenty thousand, PAR is creeping up quarter over quarter, and something has to change. At that moment two salespeople usually show up: one selling a debt collection agency , the other selling a debt collection platform. They are solving the same symptom with opposite philosophies. The agency says: give us the problem. The platform says: keep the problem, we'll give you better tools. Choosing between them is really a decision about what kind of collections capability you want to own five years from now, so it deserves more analysis than most lenders give it. What each model actually is An agency is labour plus process. You transfer batches of delinquent accounts, the agency's callers and field agents work them under a commission agreement, and recovered amounts flow back minus the f...

AI-powered debt collection platform " has become the most overused phrase in Indian lending technology, which makes it hard to know what any given vendor's system actually does once the demo ends. This article opens the box. It walks through the four working layers of a modern platform - prediction, routing, engagement and compliance - with enough detail that a collections head or CTO can tell substance from label. We'll use FrenzoFinserv's architecture as the reference, since it's the one we can describe honestly from the inside, but the layer structure applies to the category. Layer one: prediction Everything downstream depends on knowing, per account, the probability of the next EMI being missed. The scoring model consumes several signal families. Repayment behaviour is the strongest: on-time rate, partial payment patterns, bounce history, and the trajectory of all three. Bureau signals add external context - CIBIL score movement, fresh credit enquiries, utilisa...