Practical guides and field notes on loan collection, reducing defaults, and running lending operations that scale.
Why fuzzy database search can't match a spoken Tamil name to a stored one — and the phonetic skeleton we use to shortlist customers before the model sees them.
Most daily finance software still lives on one office computer and imports the agent's phone every night. Here's what that costs you — and what to ask before you buy.
What an agent chit is, why it breaks past twenty members, and how one app reminds every member, holds their location, and tracks cycles and commission.
Real stories of how paper-based lending nearly destroyed families — and what every money lender can do before it happens to them.
Run your real collection book on Vasool for 45 days for ₹700 — refundable any time. Plus the full cost breakdown of what serving you actually costs us.
How Vasool's AI credit-risk scoring works — what it reads from KYC, repayment history, and GPS visit data — and why the decision still belongs to a human.
The governance controls a regulated NBFC should demand from loan software — audit trails, maker-checker, data isolation, KYC — and what no vendor should claim yet.
Building offline-first field collections without a second, weaker write path — HTTP replay through the live router, idempotency keys, tombstones, and kill switches.
How Vasool isolates each finance company in its own container and PostgreSQL database — why we rejected shared tables with a tenant_id, and what that choice costs us.
The engineering case for Go over TypeScript on the server — deployment artefacts, per-tenant memory, money maths, and the places Go still hurts.
A practical comparison for money lenders and NBFCs — why Vasool's voice entry, offline-first design, and data ownership beat generic loan collection apps.
Practical, field-tested ways money lenders and NBFCs cut defaults — tighter daily follow-ups, real-time visibility, and proof of every visit.