AI-Driven Digital Lending & Credit Decisioning Platform
End-to-end digital lending platform with AI credit scoring, automated underwriting, and real-time fraud detection for instant personal and SME loans
Overview
RupeeFlow is a digital-first NBFC targeting underserved segments—gig workers, small merchants, and first-time borrowers who lack traditional credit histories. We built their complete lending stack from customer acquisition through collections, with AI at the core for credit decisions, fraud prevention, and personalized loan offers.
The Problem
Traditional lending excludes millions of creditworthy Indians who lack formal employment records or bureau history. RupeeFlow wanted to serve these segments profitably, but needed AI-driven underwriting to assess alternative data, prevent fraud in a high-risk segment, and automate operations for unit economics to work at small ticket sizes.
Key Challenges
Thin Credit Files
70% of target borrowers had no credit bureau history. Traditional scorecards couldn't assess these customers. Manual underwriting was too expensive for small ticket loans (₹10K-₹2L). Needed alternative data sources and ML models to assess creditworthiness.
Fraud at Scale
Digital lending attracts sophisticated fraudsters. Synthetic identities, document forgery, and first-party fraud were rampant. Manual review couldn't keep pace with application volumes. Each fraud loss erased profits from 20+ good loans.
Operational Cost Pressure
Small ticket loans need 95%+ automation to be profitable. Customer acquisition, KYC, underwriting, and disbursement had to be digital-first. Collections needed smart prioritization—can't call every delinquent account.
Regulatory Compliance
RBI digital lending guidelines required transparent pricing, grievance redressal, and data privacy. Fair lending practices needed to be auditable. Model explainability was mandatory for credit decisions.
How We Built It
Digital Onboarding & eKYC
Built mobile-first loan application with video KYC and Aadhaar-based verification. Integrated with DigiLocker for document fetch. Implemented liveness detection and face matching. Created seamless journey from application to disbursement in under 10 minutes.
AI Credit Engine
Developed ML credit models using alternative data: bank statements, UPI transactions, device data, and behavioral signals. Built credit scorecards for thin-file customers. Implemented model monitoring for drift detection. Created explainable AI framework for regulatory compliance.
Fraud Prevention Stack
Built multi-layered fraud detection: document forgery detection, device fingerprinting, network analysis for fraud rings, and velocity checks. Implemented real-time transaction scoring. Created investigation workbench for fraud analysts.
Collections Intelligence
Developed propensity-to-pay models for prioritizing collection efforts. Built automated communication workflows (SMS, WhatsApp, email). Implemented smart calling with optimal contact time prediction. Created settlement offer engine with personalized terms.
Solution Highlights
Alternative Data Credit Scoring
ML models analyze bank statements, UPI history, and behavioral data to assess creditworthiness for thin-file customers. Enables lending to segments traditionally excluded from formal credit.
Real-Time Fraud Detection
Multi-layered fraud stack catches document forgery, synthetic identities, and fraud rings. Sub-second decisions block fraudsters without adding friction for genuine customers.
10-Minute Disbursement
End-to-end digital journey from application to bank transfer in under 10 minutes. Video KYC, instant credit decision, and automated agreement execution.
Smart Collections
AI prioritizes collection efforts based on likelihood to pay. Personalized settlement offers improve recovery. Automated communications at optimal times maximize contact rates.
Technical Deep Dive
The credit engine uses gradient boosting (XGBoost) for credit scoring with 200+ features extracted from bank statements, UPI transactions, and device signals. Models are retrained monthly with performance monitoring for drift. Explainability is provided via SHAP values, generating plain-English reasons for each credit decision. Fraud detection combines rule-based checks for known patterns with ML models for emerging fraud types. Document verification uses computer vision for forgery detection, achieving 98% accuracy on tampered documents. The collections system uses survival analysis models to predict time-to-payment, prioritizing accounts with highest expected recovery value.
AI Capabilities
Alternative Credit Scoring
ML models for thin-file customers using non-bureau data
Document Verification
AI-powered detection of forged and tampered documents
Fraud Ring Detection
Network analysis identifying connected fraudulent applications
Income Estimation
Inferring income from transaction patterns for self-employed
Collection Optimization
Prioritizing accounts by propensity to pay
Personalized Offers
Right loan amount and terms for each customer
Technology Stack
Credit Models
Fraud Detection
Backend
Mobile
Integration
Infrastructure
Results & Outcomes
-60%
Loan approval time
From days to under 10 minutes
-40%
Default rate reduction
Better credit decisions with alternative data
70%
First-time borrowers
Serving thin-file customers profitably
₹500Cr+
Loans disbursed
Platform volume in first year
98%
Fraud detection accuracy
Catching document forgery and synthetic IDs
95%
Automation rate
Minimal manual intervention required
“Boolean and Beyond built us the lending stack that lets us serve customers everyone else ignores. Our credit models see what traditional bureaus miss, and our fraud detection is best-in-class.”
Founder & CEO
RupeeFlow
Services Used for the Client Product
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