AI-Powered UPI Credit Scoring Engine | CARD91
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AI-Powered UPI Credit Scoring
AI/ML-driven behavioural credit scoring engine that leverages UPI transaction data to deliver actionable insights for informed and inclusive lending decisions.
Key Features
AI That Scores Beyond the Bureau
AI/ML-Based UPI Credit Scoring
Generate a behavioural credit score (0–100) based on machine learning analysis of UPI transaction data.
Real-Time Score Generation
Facilitate seamless onboarding with on-demand or batch-based score computation.
Dynamic Approval Matrix
Apply configurable risk thresholds to automate approvals, trigger manual reviews, or decline applications.
Flexible Eligibility Filters
Customize baseline criteria such as income, demographic details, transaction details to define target segments.
Comparative Analysis with Bureau Scores
Benchmark the BRE Score alongside traditional credit bureau ratings to assess credit uplift and risk segmentation.
Benefits
AI-Led Inclusion & Growth
Extend Financial Inclusion
Enable access to credit for individuals lacking traditional credit bureau history.
Reduce Portfolio Risk
Utilize real-time UPI behavioural data to enhance underwriting precision and mitigate default risk.
Drive Business Growth
Increase approval rates and lifetime value by targeting high-potential customer segments with greater accuracy.
Access New-to-Credit Customers
Evaluate customers with thin-file or no bureau records using behavioural transaction data.
Minimize Bureau-Based Limitations
Enhance decision-making speed and accuracy by utilizing dynamic behavioural data over static credit reports.
Support Regulatory Mandates
Meet financial inclusion goals by responsibly expanding credit to underserved segments.
How it Works
Your AI Credit Engine in Action
Refine Scoring Parameters
AI/ML models evaluate historical UPI data to determine optimal variables and weightages.
Configure Eligibility Criteria
Establish minimum income, transaction details, demographic details to filter relevant customer profiles.
Generate UPI Scores
Compute a credit score (0–100) for each customer either individually or in bulk.
Classify Risk Segments
Segment customers into High, Medium, or Low risk groups based on scoring thresholds.
Apply Approval Matrix
Link each segment to corresponding decision rules for approval, review, or rejection.
Resources
Our Latest Partnerships & Announcements
Latest Press Release
Blog Posts
Routing Breakdowns in Digital Onboarding: How Banks and NBFCs Can Identify Them Early
Learn how banks and NBFCs can identify routing breakdowns in digital onboarding before they affect review load and decision quality.
BFSI Onboarding Escalation Design: When a Case Should Move to Review or Higher Scrutiny
Learn how banks and NBFCs can improve BFSI onboarding escalation design for better review routing and stronger control.
How Banks and NBFCs Can Reduce Review Backlogs Without Weakening Control
Learn how banks and NBFCs can reduce review backlogs in BFSI onboarding without weakening control or slowing decisions.
What Good Clarification Workflow Design Looks Like in Digital Onboarding
Learn what good clarification workflow design looks like in digital onboarding and how it helps reduce unnecessary review.
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Monepeak Fintech Private Limited
1st Floor, 1614, Enzyme 7th cross, 19th Main Rd, Sector 1, HSR Layout, Bengaluru, Karnataka 560102