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Case study · Banking / NBFC

Designing an explainable credit decision flow for MSME lending

Illustrative engagement structure showing how a lender can move from manual document review to policy-governed, explainable decisioning with human review.

Illustrative engagement structure. Client details and outcomes are placeholders and will be replaced with an approved, verifiable client story.
Client
[CLIENT NAME — placeholder]
Industry
Banking / NBFC

Challenge

A lender’s MSME working-capital applications required manual review of bank statements and tax filings, creating long turnaround times and inconsistent decisions.

Context

Placeholder context — replace with an approved description of the client, their lending products, constraints and regulatory environment.

Approach

Credit policy was codified as versioned rules, consented cash-flow data replaced manual statement review where available, and an interpretable model provided risk estimates with reason codes. Borderline cases were routed to underwriters.

Architecture

Solution architecture

Reference architecture
  1. Consent & data acquisition
  2. Document forensics
  3. Policy rules
  4. ML scoring
  5. Review workbench
  6. Audit ledger

Implementation

  • Policy workshop and rule codification
  • Feature engineering on consented transaction data
  • Model development with out-of-time validation
  • Shadow deployment alongside manual process

Security

  • Field-level encryption of personal data
  • Role-based access to case data
  • Immutable decision audit trail

Technology

  • Python
  • LightGBM
  • SHAP
  • PostgreSQL
  • Kafka

Outcome

Placeholder outcome — replace with client-approved, verifiable results. Do not publish metrics without written client approval.

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