Client Overview
A leading product vendor providing end-to-end loan management solutions for several top banks in India.
The client supports high-volume lending journeys across retail and MSME segments. While their existing rule-based decision engine was reliable for standard applications, it failed to capture the potential of borderline or unconventional borrower profiles. This resulted in missed opportunities, reduced approval rates, and leakage of creditworthy customers to competing lenders.
The Challenge
The client's loan decisioning framework relied entirely on fixed eligibility rules, leaving no room for contextual interpretation or mitigant-based approvals. Key challenges included:
- Static rule checks (e.g., CIBIL ≥ XX, FOIR ≤ YY, GST trends ≥ ZZ) leading to rigid decision outcomes
- Single-rule failure = auto-reject, even when the applicant may be creditworthy with additional information
- Missed surrogates such as co-applicant availability, collateral, salary stability, employer category, or repayment patterns
- Loss of "near-miss" applicants who could qualify with minor adjustments or additional documentation
- High leakage to competitors, especially digital lenders offering flexible mitigant-based approvals
These constraints limited portfolio growth, reduced approval efficiency, and created poor customer experiences with dead-end rejection journeys.
Our AI Solution
To expand the client's lending universe and reduce unnecessary rejections, we implemented an AI model that dynamically identifies mitigants and alternate eligibility paths to convert an initial "reject" into an "eligible with mitigants" decision.
This intelligent system evaluates borrower data, behavioural information, and portfolio patterns to show how a customer can still qualify—without relaxing risk thresholds.
1. Dynamic Eligibility Path Identification
The AI evaluates multiple data combinations to find alternate ways the applicant could qualify (e.g., co-applicants, collateral, income surrogates).
2. Missing Data Detection
Identifies missing but relevant information such as:
- Co-applicant / guarantor availability
- Collateral options
- Risk-based pricing flexibility
- Salary consistency and employment tier
3. Mitigant Recommendation Engine
Suggests specific mitigants such as:
- Tenor modification
- Reduced loan amount
- Adjusted FOIR targets
- Risk-based interest rate
- Additional documentation for risk clarity
4. Risk Quantification & Improvement Scoring
Calculates how much the borrower's risk score improves when suggested mitigants are applied, ensuring credit discipline is never compromised.
5. "No Dead-End" Borrower Experience
Instead of ending the journey at "Rejected," the system provides an actionable path: "Here's what you need to qualify."
Importantly, AI does not relax rules — it discovers additional data and mitigants that legitimately move the borrower into eligibility.
Impact & Key Results
The AI-driven market extension framework delivered significant business value:
- Captured 'Near-Miss' Borrowers — Recovered applicants who would have been rejected by a static rules-based system.
- Increased Portfolio Growth Without Raising Risk Appetite — Enabled safer approvals using justified mitigants and documented risk improvements.
- Reduced Customer Leakage to Competitors — Especially valuable in digital personal loan journeys where customers drop off after instant rejections.
- Enabled Cross-Sell & Product Switching — Provided alternatives like:
- PL → Secured PL
- HL → LAP
- MSME OD → WC
- Improved Customer Experience — Replaced the dead-end "Rejected" screen with a guided, supportive "Here's how you can qualify" pathway.
- Enhanced Brand Perception — Positioned the lender as a "bank that helps customers qualify", not simply reject them.
Increase Approval Rates with AI-Driven Mitigants
Learn how leading lenders are expanding their markets while maintaining risk discipline through intelligent mitigant identification.
Connect with us to explore how AI can unlock your lending potential.
