## BLITZSCORE

## 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](/content/blog/routing-breakdowns-digital-onboarding/index.html)

## 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](/content/blog/bfsi-onboarding-escalation-design/index.html)

## 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](/content/blog/reduce-review-backlogs-bfsi-onboarding/index.html)

## 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](/content/blog/clarification-workflow-design-digital-onboarding/index.html)

## Learn what good clarification workflow design looks like in digital onboarding and how it helps reduce unnecessary review.

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## Member Of

**Monepeak Fintech Private Limited**

1st Floor, 1614, Enzyme 7th cross, 19th Main Rd, Sector 1, HSR Layout, Bengaluru, Karnataka 560102