€1.1M saved per year — across fraud detection, collections, and loan documentation.
Fraud, collections, and loan documentation — all manual, all expensive
A non-banking financial institution ran fraud detection, debt collection, and loan documentation as separate manual workflows. Fraud was caught after the fact, when caught at all. Collections required full-time agent time per case. Loan documentation tied up specialists who should have been doing underwriting.
Real-time fraud detection, automated collections, and document intake — replacing manual workflows end-to-end
- Real-time AI pattern detection across transactions, flagging fraud before payout
- Multi-channel collections outreach (call, email, WhatsApp) with payment negotiation logic
- Automated loan document intake, OCR extraction, validation, and structured handoff
- Smart escalation to human specialists for sensitive or complex cases
Lower operational costs, faster collection cycles, fraud caught earlier
~€1.1M in annual savings across the three workflows combined — direct cost savings (replaced manual processing), avoided losses (earlier fraud detection), and cash flow improvements (faster collection cycles).
What AI automation means for non-banking financial institutions
Non-banking financial institutions — lenders, leasing companies, brokerages — typically carry significant cost in manual fraud screening, collections, and loan documentation. AI automation across these three workflows tends to compound: each one saves cost individually, and together they free specialists for higher-value work.
What we built (technical)
Real-time fraud pattern detection model integrated with transaction systems, multi-channel collections automation engine, document intake and OCR pipeline integrated with loan management system. Audit-grade logging throughout. See `/technical/` for engineering details.