01 PROBLEM STATEMENT

Your Credit Model Makes Decisions. Can it Explain Them?

A leading private sector bank had invested significantly in credit scoring models FICO based, in-house, or watsonx-powered to automate lending decisions. But when a declined applicant asked why, the bank had no structured answer. Officers gave informal explanations from memory. Complaint responses took days to draft. RBI fair lending audits required manual reconstruction of decision rationale across thousands of applications. With DPDP and the EU AI Act raising the bar on algorithmic accountability, the bank’s credit models were becoming a regulatory liability not because the decisions were wrong, but because they could not be explained.

02 CURRENT CHALLENGES

Challenges in Credit Risk Assessment and Decision Making

03 SOLUTION OVERVIEW

STAR’s Approach – AINE Credit Decision Explainability

STAR Systems deployed a SHAP based explainability layer that wraps the bank’s existing credit scoring engine whether FICO, in-house, or watsonx without replacing or retraining it. Every credit decision now generates a plain-language explanation in seconds, identifying the top factors that drove the outcome. Explanations are stored in DB2 on IBM PowerVS for a complete audit trail. Declined applicants receive clear, respectful explanations via the customer portal or letter generation system. No ongoing ML ops required the SHAP wrapper is stateless and updates automatically when the underlying model is retrained.

04 WORKFLOW PROCESS

Step-By-Step: How Every Credit Decision Becomes Explainable

Step 1 – Credit Application: Applicant submits loan or credit card application. CBS provides applicant data context to the scoring engine.

Step 2 – Model Scoring: Existing credit model scores the application FICO, in-house, or watsonx. No change to the scoring engine.

Step 3 – SHAP Explanation: SHAP wrapper intercepts the model output and computes feature-level contribution scores for every decision.

Step 4 – Plain-Language Draft: Top contributing factors translated into plain-language explanation – clear, respectful, and regulator-ready.

Step 5 – Applicant Delivery: Explanation delivered to applicant via customer portal or letter generation system within seconds of decision.

Step 6 – Audit Trail Stored: Full explanation, SHAP values, model inputs, and decision stored in DB2 on IBM PowerVS for compliance review.

05 KEY FEATURES

What the Platform Does

Wraps Any Existing Credit Model:

SHAP layer wraps FICO, in-house, or watsonx scoring engines via API. No model replacement, no retraining, no disruption to live credit decisioning.

Plain Language Decline Explanations:

Every decline generates a clear, respectful explanation in plain language identifying the top factors. Readable by the applicant, the officer, and the regulator.

RBI Fair Lending Compliance:

Explanations documented automatically for every decision. RBI fair lending audits answered with a structured, searchable explanation log not manual reconstruction.

DPDP and EU AI Act Readiness:

Every automated credit decision is documented, non-discriminatory, and explainable – meeting DPDP 2023 and EU AI Act requirements for algorithmic accountability.

Zero Ongoing ML Ops:

The SHAP wrapper is stateless. No model management overhead for the bank’s team. STAR provides updates automatically when the underlying credit model is retrained.

Compliance-Grade Audit Storage:

Full explanation, SHAP values, model inputs, and approval stored in DB2 on IBM PowerVS on-premise. Quarterly explanation quality review by compliance team.

06 BUSINESS OUTCOMES

What Changes After Go Live

07 REAL-WORLD SCENARIO

A Day in the Life – Before and After

08 ROI AND VALUE JUSTIFICATION

Why the Numbers Work

09 NEXT STEPS