Agentic AI for Indian banks, NBFCs, SFBs.
Loan underwriting. AML alert review. KYC enhancement. Exception triage. Four workflows where Indian banks burn the most reviewer hours per crore of business — and where RBI's audit expectations make automation feel risky. Vihaya solves the audit primitive first, then automates.
Workflows we automate
Retail loan underwriting
Read the application, the bureau report, the income proofs, and the policy. Decide approve / decline / refer with citations. Personal, two-wheeler, gold, agri-credit, education.
AML alert review
Read the alert, the transaction history, the customer profile, the FIU-IND typology guidance. Output: clear / refer / SAR-recommend with cited rationale.
KYC re-verification
Compare KYC documents against the existing record. Flag re-verification needs grounded in the bank's CDD policy + RBI KYC Master Direction.
Trade-finance exception triage
Cross-document reconciliation (LC, invoice, BL, packing list) for trade-finance exception handling with cited discrepancies.
Why RBI alignment is the unlock
Indian bank technology teams have been able to automate decisioning for years. The blocker has been the audit chain. RBI examiners want to walk into a bank, pick a specific loan-decline from three years ago, and see exactly which policy clause, which bureau report line, and which exception path produced that decision. Most ML systems can't answer that question.
Vihaya's audit primitive is exactly that: every decision is one immutable row linking to the policy clauses cited, the bureau-data points referenced, the agent run that produced it, and the human reviewer who confirmed or overrode it. Reconstructable from cold storage years later. That's the ask that turns 'AI for banks' from a vendor pitch into a deployable system.
Products in scope (illustrative)
Pilot scope per product. Touchless-rate targets are tuned during each pilot's shadow run; Vihaya is pre-revenue and no paid pilot has yet completed, so we don't publish ranges as fact.
| Product | Pilot scope | Touchless rate |
|---|---|---|
| Personal loan | Salaried, ticket-size ≤ ₹5L | Tuned per pilot |
| Two-wheeler loan | Salaried + self-employed | Tuned per pilot |
| Gold loan | First-cycle and renewal | Tuned per pilot |
| AML L1 review | Sanctions / threshold / typology alerts | Tuned per pilot |
| KYC re-verification | Periodic CDD refresh | Tuned per pilot |
Banking & NBFC FAQ
Does Vihaya satisfy RBI's IT outsourcing framework?
Yes. RBI's 2023 Master Direction on Outsourcing of IT Services requires data localisation, examiner audit rights, exit-management plans, and BCP/DR provisions. Vihaya deploys inside your bank's VPC (AWS Mumbai, GCP Mumbai, or Azure South India) — no data egress to Vihaya. The audit trail supports examiner inspection of any specific decision. Standard pilot SOW includes the right-to-audit clause and exit-management plan.
What about cooperative banks and small-finance banks?
Same engagement shape. SFBs and large urban cooperative banks have the volume to justify the pilot economics — typical retail-credit volume of 1L+ files per month per bank is in scope. The pilot is structured around one product (personal loan, two-wheeler loan, gold loan, agri-loan) before extending.
Can Vihaya read CIBIL, Experian, Equifax reports?
Yes. The Context Mesh ingests structured credit-bureau reports alongside the unstructured policy and application text. The agent's decision rationale cites both the bureau-data point and the policy clause it triggered.
How does this compare to existing decisioning engines like FICO Origination Manager?
Rule engines like FICO OM are still excellent for hard-policy filters (income thresholds, score cutoffs). Vihaya layers on top — it handles the document-parsing, judgement-grade evaluations, and exception cases that rule engines cannot decide. Most engagements end up with FICO handling 30% of decisions deterministically and Vihaya handling the rest with explainable, escalation-protected output.
Is this safe for AML alert review under PMLA?
AML review under the Prevention of Money Laundering Act and FIU-IND guidance requires explainable, audited decisions. Vihaya's primitive matches: every alert disposition is one immutable row with citations to the underlying transaction patterns and policy bulletins. Below the confidence floor, the case routes to your compliance analyst — never auto-cleared.
Want to see this in your environment?
30-minute discovery call. Draft SOW within 5 business days.
Talk to us about a pilot →