Financial Services · Loan underwriting automation
Your underwriting queue is your origination ceiling
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.
The first call is free · 45 minutes · no obligation
What we build
What an automated underwriting deployment covers
Underwriting automation is not one model — it is a pipeline of data ingestion, scoring, rule execution, and exception routing. We own the whole chain.
Bureau pull and statement parsing
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.
Alt-data scoring for thin-file applicants
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.
Credit policy rule engine
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.
Automated document verification
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.
Exception routing and officer workflow
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.
Audit trail and regulatory reporting
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.
Receipts
Where this is already running
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.
Manual underwriting replaced with a policy-driven decisioning pipeline
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.
Dogfooding
We run our own hiring through the same AI rigour
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.
Screens every Banao engineering hire — consistent, auditable, fast.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When underwriting automation is the wrong starting point
Automation is not always the first lever. We will tell you if your situation calls for something else first:
- Inconsistent credit policy: if your written policy has gaps or internal contradictions, automating it encodes the inconsistency at speed. Policy cleanup before automation is the right order.
- Data quality gaps: if bureau integration is unreliable or statement data arrives in too many formats to parse cleanly, the pipeline needs a data-quality pass before scoring adds value.
- Low origination volume: below a certain monthly volume, the fixed cost of the pipeline does not pay back in cycle-time savings. We will tell you the number before you commit to a build.
How we start
How we start — map your decision flow before we build
We do not quote an underwriting pipeline off a template. We look at your credit policy, your LOS, and your application data first.
- 01
AI Discovery Sprint
2 weeks · fixed price
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.
- 02
Build
Bureau integration, statement parsing, rule engine, alt-data models, document verification, and officer workflow — delivered and tested in your LOS environment, not a sandbox.
- 03
Production & monitoring
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.
FAQ
Frequently asked questions
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.
Get started
Show us your credit policy and your LOS
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 Discovery Sprint