Problem Statement

A digital lending FinTech platform serving both salaried and self-employed users across India and Southeast Asia, disbursing over $400M in annual loan volume with over 5 million active users.

The client grappled with:

• High Default Rate in New-to-Credit (NTC) Segment: Lack of traditional credit history made underwriting difficult. Default rates in NTC borrowers reached ~15%, impacting portfolio quality.
• Reactive Fraud Detection Mechanism: Rule-based fraud engine detected anomalies post-disbursal, leading to $2.1M/year in fraud write-offs from synthetic identities and mule accounts.
• Inconsistent KYC & Underwriting: Manual scrutiny and rigid scoring caused 20% false positives (good borrowers rejected) and 25% false negatives (bad loans approved).
• No Real-Time Decisioning: Batch-processed loan approvals led to 2–4 hour turnaround times, reducing customer satisfaction and conversion rates.

These challenges resulted in higher credit losses, operational inefficiencies, slower loan disbursement, and missed growth opportunities in a competitive market.

Solution Provided by FinFusion

FinFusion implemented an integrated AI/ML-powered Decision Intelligence Layer seamlessly embedded into the client’s lending tech stack:

• Advanced Risk Modelling: Ensemble models (XGBoost + DNN) trained on 120+ features including alternative data, device metadata, app behavior, transaction history, UPI logs, and bureau signals.
• Dynamic Credit Segmentation: Self-learning credit risk segments (A/B/C/D) with monthly threshold recalibration.
• Fraud Ring Detection: Graph Neural Network (GNN) identified fraud rings by linking phone numbers, PANs, IPs, and bank accounts.
• Real-Time Decisioning: Integrated instant scoring at pre-KYC and pre-disbursal stages to block high-risk applicants before loan issuance.
• Automated Document Verification: OCR and NLP for instant validation of PAN, Aadhaar, and payslips, reducing manual review.
• Enhanced KYC Security: Computer vision models for liveness and deepfake detection during selfie-KYC.
• Collections Optimization: Logistic regression models segmented delinquent borrowers by propensity-to-pay for targeted recovery campaigns.

Benefits Realized

KPI	Before	After	Impact
Default Rate (NTC Segment)	~15%	~6.2%	↓ 58%
Fraud Write-offs (Annual)	$2.1M	$420K	↓ 80%
False Positives (Good Loans Rejected)	20%	7%	↑ 65% reduction
Loan Approval TAT	2–4 hours	<2 minutes	⏩ 60x faster
Collections Recovery (D30+)	37%	61%	↑ 65%
Underwriting Cost per Loan	$3.50	$0.85	↓ 75%

Qualitative Gains

• Customer Delight: Instant approvals and proactive fraud prevention boosted NPS and app ratings.
• Lender Confidence: AI-driven underwriting earned trust from banking partners and NBFCs.
• Regulatory Preparedness: Built-in explainability (XAI) ensured auditable decision trails for RBI/SEBI compliance.
• Continuous Learning: Auto-retraining pipelines refreshed models monthly to adapt to new fraud trends.

Conclusion

This AI/ML transformation empowered the client to evolve from rule-based lending to dynamic, intelligent credit lifecycle management— spanning risk scoring, fraud prevention, KYC, and collections. The result: a safer, faster, and more inclusive lending model, perfectly aligned with next-gen digital finance goals.