Banking & Lending

AI Underwriting Platform

An explainable credit and fraud decisioning platform for banks and NBFCs, with human review and a complete audit trail.

In development

The problem

The challenge

Lenders want to serve more borrowers — especially MSMEs and thin-file applicants — but manual underwriting is slow and expensive, document fraud is increasingly sophisticated, and any automated decision must be explainable to customers, auditors and regulators.

Current industry pain points

  • Days of manual review for applications that could be decided in minutes
  • Edited bank statements and fabricated documents passing visual checks
  • Thin-file applicants declined because risk cannot be measured, not because it is high
  • Credit policy changes that take weeks to implement and are hard to audit
  • Model decisions that cannot be explained to the applicant or the regulator

The solution

How it works

The platform combines consented data acquisition, document forensics, deterministic policy rules and interpretable machine-learning models in a single decision flow. Clear cases are decided automatically within confidence bands defined by your credit policy; everything else is routed to underwriters with the full evidence and explanation in front of them.

Decision flow
  1. Application intake API
  2. Consent & data acquisition
  3. Document AI & forensics
  4. Fraud engine (rules → ML)
  5. Credit scoring (policy + ML)
  6. Explainability (reason codes)
  7. Decision routing
  8. Human review workbench
  9. Immutable audit ledger

In detail

Inside the platform

01

AI and machine-learning capabilities

Gradient-boosted models trained on cash-flow, bureau and behavioural features provide a calibrated risk estimate. Models are selected for accuracy and interpretability on tabular credit data.

  • Cash-flow features from Account Aggregator and bank statements
  • Point-in-time-correct feature computation to prevent leakage
  • Out-of-time validation with discrimination and stability metrics
02

Fraud detection

Fraud checks run before credit assessment. Deterministic rules catch known patterns within milliseconds; models and document forensics address the rest.

  • Bank statement and payslip tamper detection
  • Source-data reconciliation against consented data
  • Velocity, device and network signals across applications
03

Risk assessment

Credit and fraud risk are scored separately and combined by policy, so each can be governed, tuned and explained independently.

04

Decision engine

Credit policy is expressed as versioned rules with dual approval. Every decision records the policy version, model version and inputs used.

  • Semantic versioning of policy and models
  • Simulation of policy changes against historical applications
  • Configurable confidence bands for automation
05

Human review

Borderline and high-risk cases are routed to a review workbench with prioritised queues, evidence views and mandatory rationale codes for every override.

06

Auditability

An append-only, cryptographically verifiable record captures inputs, features, scores, reason codes, model versions and reviewer actions for each application.

Capabilities

Key capabilities

  • Explainable decisions

    Reason codes derived from feature contributions for every outcome.

  • Policy as code

    Versioned, testable, dual-approved credit policy.

  • Document forensics

    Detection of edited and fabricated financial documents.

  • Human-in-the-loop

    Structured review for cases outside automation bands.

  • Fairness controls

    Protected attributes excluded and disparate impact monitored.

  • Model governance

    Registry, shadow deployment and drift monitoring.

Trust

Security, integration and outcomes

Security by design

  • Data residency within Indian cloud regions
  • Field-level encryption of personal data with customer-managed keys
  • Tenant isolation for multi-lender deployments
  • Zero-trust service communication with mTLS
  • Immutable audit logging

Integrations

  • Credit bureaus
  • Account Aggregator framework
  • GSTN and Udyam data
  • eKYC and CKYC providers
  • Loan origination and management systems
  • Core banking via APIs or events

Business outcomes

  • Faster decisions for straightforward applications
  • Consistent, documented application of credit policy
  • Underwriter capacity focused on cases needing judgement
  • Evidence ready for audit and supervisory review

Outcomes depend on each organisation’s data, products and processes. We do not publish accuracy or ROI figures without independently verifiable evidence.

FAQ

Frequently asked questions

Is the platform available today?

The platform is in active development. We are working with design partners and can deliver its components as tailored implementations today. Contact us to discuss a briefing or pilot.

Can it run in our own cloud environment?

Yes. The architecture supports deployment in a lender’s own cloud account to meet data-residency and control requirements.

Explore the AI Underwriting Platform

Book a technical briefing with our team to discuss your lending products, data sources and decisioning requirements.