Automated Underwriting & Risk Scoring Analytics
Vishal Rajput
Automated Underwriting & Risk Scoring Analytics
Project Summary
This project focused on enabling automated underwriting and credit decisioning through analytics-driven risk scoring for credit unions. The objective was to help lending teams make faster, more consistent, and data-backed credit decisions while maintaining a healthy balance between portfolio growth and risk control.
The solution leveraged historical credit behavior, repayment patterns, and member product usage data to generate member-level predictive risk scores. These scores were designed to integrate seamlessly into existing underwriting workflows, minimizing the need for major system or policy changes.
Project Overview
Analyzed historical loan performance and member credit behavior
Designed predictive risk scoring models for lending decisions
Segmented members by risk tier to support policy-driven approvals
Ensured outputs aligned with existing underwriting processes
Focused on scalability and ease of operational adoption
Key Responsibilities
1. Data Analysis & Feature Engineering
Analyzed data related to:
Loan repayment history and delinquency patterns
Credit utilization and balance trends
Product holdings and member tenure
Payment behavior and account stability
Engineered risk indicators such as:
Payment consistency score
Utilization risk metrics
Delinquency frequency indicators
2. Risk Scoring & Segmentation
Developed member-level predictive risk scores
Segmented members into low, medium, and high-risk tiers
Enabled risk-based decisioning for approvals, limits, and pricing
3. Underwriting Analytics Support
Provided analytics insights to support:
Automated approval and decline logic
Manual review prioritization
Credit policy alignment with risk segments
4. Business Insights & Recommendations
Delivered risk segmentation insights to optimize:
Credit policy thresholds
Portfolio growth strategies
Loss prevention measures
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Posted Jan 18, 2026
Developed predictive risk scoring models for automated credit decisioning.
