Financial Services · Loan underwriting automation
Banao builds automated loan underwriting that runs bureau pulls, statement parsing, alt-data scoring, and policy rule execution in minutes — not days — and hands a decision or a pre-assembled exception to your credit officer with every step documented.
The system wires into your existing LOS and core. It does not replace your credit policy; it enforces it faster and more consistently than a manual queue ever will.
Underwriting automation is not one model — it is a pipeline of data ingestion, scoring, rule execution, and exception routing. We own the whole chain.
Automated bureau integration and bank-statement OCR extract repayment history, obligations, and cash-flow patterns in seconds, eliminating the manual data-entry step that delays every application.
GST returns, UPI transaction history, and utility data extend credit decisioning to applicants with short bureau histories, scored with a model your credit team can interrogate — not a black box.
Your written credit policy is encoded as executable rules — LTV caps, income multiples, sector exclusions — so every application is assessed against the same criteria your credit committee approved.
Income proof, property documents, and identity papers are verified by a document intelligence layer that flags anomalies before they reach a credit officer, cutting fraud exposure and reducing review time.
Applications outside auto-approval bands are routed to a credit officer queue with every data point assembled — no missing documents, no manual lookups — so exceptions receive decisions, not stalls.
Every decision — automated or manual override — is logged with the data and model version that produced it, so you can answer any regulator or internal audit query without reconstructing a paper trail.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. Some clients in this vertical are described without identifying them, as their contracts require.
An NBFC lender
Applications that took two to three days of manual bureau, statement, and document checks were reduced to a same-session decision for eligible applicants, with exceptions pre-assembled and routed to officers with the full credit file.
Banao operates a ~300-person engineering company on its own AI products. InterviewGod screens every engineering hire we make. Vikaas runs our own demand-generation pipeline. A system we would trust with our own operations is the bar we apply before shipping it to yours.
When we say an automated decisioning system can be auditable, explainable, and compliant — we are drawing on what it takes to run AI that your own team scrutinises daily, not just a client's.
Automation is not always the first lever. We will tell you if your situation calls for something else first:
We do not quote an underwriting pipeline off a template. We look at your credit policy, your LOS, and your application data first.
We map your existing credit policy and decision flow, audit your data sources and LOS integrations, and return a feasibility report with an auto-approval rate estimate and a build scope — yours to keep. If you proceed, the Sprint cost is credited against the project.
Bureau integration, statement parsing, rule engine, alt-data models, document verification, and officer workflow — delivered and tested in your LOS environment, not a sandbox.
Live deployment with model drift monitoring, approval-rate reporting, and a review queue for your credit team. Changes to your credit policy are updated in the rule engine, not a separate change-management project.
Can it work with our existing LOS?
Most LOS platforms expose APIs or have integration points Banao has mapped. The Discovery Sprint establishes the integration path for your specific system — including legacy platforms without a standard API — before any build cost is committed.
What happens to applications the model cannot auto-decide?
Applications outside the auto-approval and auto-decline bands are routed to a credit officer queue with every data point assembled — bureau output, parsed statement summary, alt-data score, policy flags, and document verification status — so the officer makes a decision rather than hunting for files.
How is the credit policy encoded and updated?
Your credit policy is translated into a rule engine your credit team can read and maintain. When policy changes — LTV caps, income multiples, sector exclusions — the rule engine is updated directly, with a change log and staged deployment so you can test a policy change before it goes live.
Will regulators accept AI-driven credit decisions?
Every decision is logged with the data inputs, model version, and rule set that produced it. The audit trail satisfies examination patterns we have seen from RBI and SEBI in practice. Where an explanation is required for an adverse decision, the system generates it from the logged rationale.
How do you handle thin-file applicants without a bureau history?
Alt-data models — UPI transaction history, GST returns, utility payment patterns — are layered in for applicants with limited bureau history. The score is explainable and tagged so officers know which data source drove it, and the model is tested against your historical approval and default outcomes before it goes live.
In 45 minutes we will tell you where underwriting automation earns its cost and what your data and integration setup would need to make it work.
Book a 45-min scoping call
