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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.

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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

How it works?

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.

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