Ignosis AI has announced the launch of its Multi-Agentic, Hyperpersonalised AI Collections Platform for BFSI, a next-generation debt recovery solution powered by Financial Data Intelligence and Account Aggregator Intelligence.
The platform, launched from Ahmedabad, aims to address a persistent gap in loan collections, where thousands of Indian borrowers miss their EMI deadlines each month not because they cannot pay, but because nobody reached out to them in time.
The company, which has spent years embedded in the operations of Indian lenders building financial data intelligence and AI solutions across the lending stack, has built a new approach to loan collections after watching the problem go unsolved.
The platform is already processing over 2 lakh cases monthly and handles the full arc of borrower conversations end-to-end, covering multilingual outbound pre-due and post-due collections, payment reminders, promise-to-pay follow-ups, payment collections, dispute resolution, and seamless human handoffs, all within a single platform.
Nirav Prajapati, Co-Founder and CEO, Ignosis AI, said, “The collection ecosystem is still largely driven by broad segments, fixed rules, and call volumes, these outcomes improve when lenders understand the borrower behind the account. We are enabling collections that can adapt and execute based on each borrower’s context, payment intent, and circumstances by combining real-time, borrower-consented financial signals from Account Aggregator with multi-agent AI. That’s a fundamentally different way of thinking about debt recovery and AI orchestration for BFSI in India.”
What makes the proposition compelling for institutional buyers is not any single capability but the architecture beneath them. The platform runs tens of thousands of simultaneous calls at sub-300 millisecond latency, absorbing month-end peak loads that routinely bring conventional diallers down.
Outbound campaigns are prioritised using Account Aggregator and GST signals, with the highest-intent borrowers reached first, not last, while retry timing and attempt cadence are model-driven per borrower rather than fixed.
Over 50 voice personas are matched dynamically to a borrower’s DPD stage and profile, with collections-grade conversation quality across regional languages including Tamil, Telugu, Marathi, Bengali, Kannada, and Malayalam.
When a negotiation turns complex, covering part-payment, settlement, or a broken promise-to-pay, the platform handles it using real borrower financial signals and cashflow context rather than static scripts, and when a case needs a human, the agent receives full context instead of a cold transfer.
For regulated lenders weighing adoption, the compliance posture may matter as much as the product itself. The platform is built to India’s Digital Personal Data Protection Act standards, with all models hosted domestically, full data residency, and every call auditable and traceable. DND scrubbing, TRAI regulations, and RBI fair practices compliance are enforced by default.
Every conversation produces structured output covering disposition, sentiment, objection type, and promise-to-pay likelihood, feeding what Ignosis describes as a borrower intelligence layer that deepens with each call. Native integrations with LOS, LMS, and core banking systems bring the stated deployment timeline to two weeks.
“With our multi-agentic, hyperpersonalised AI Collections Platform for BFSI, every collection conversation is informed by prior interactions, account data, and real borrower signals, making each call feel less like a collections attempt and more like a continuation of an informed borrower engagement journey,” the company noted.
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