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This project offers a 360° borrower analysis using Tableau dashboards on 3+ lakh loan applications (₹184B exposure). It identifies default hotspots by age, education, occupation, and loan type, with an 8.07% default rate. Insights help institutions reduce risk, boost profitability, and make smarter loan approvals.

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gulnaaz-data-analyst/End-to-End-Loan-Lifecycle-Analysis

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📊 End-to-End Loan Lifecycle Analysis

🔗 Project by Gulnaaz Ali


📌 Project Overview

Financial institutions face challenges in managing loan portfolios and assessing borrower risk. This project provides a 360° analysis of the loan lifecycle — from applications and borrower demographics to repayment patterns and default risks.

Using interactive dashboards and integrated datasets, the project highlights borrower behavior, credit performance, repayment delays, and default hotspots, enabling data-driven decision-making for risk mitigation and profitability.


🎯 Objectives

  • Build a 360° Customer View by analyzing demographics, loan applications, and financial history.
  • Assess borrower credibility using past repayment behavior.
  • Monitor loan repayment trends to detect delays and defaults.
  • Identify high-risk borrower groups for early intervention.
  • Provide strategic recommendations for financial institutions.

📊 Dashboards

🔹 Current Loan Portfolio Performance & Default Insights

Dashboard 1

🔹 Loan Distribution & Borrower Segmentation

Dashboard 2

🔹 Previous Credit Performance & Repayment Trends

Dashboard 3


🔍 Key Insights

  • Applications: 3+ lakh borrowers, total credit exposure of ₹184 billion.
  • Default Rate: ~8.07% overall; higher among <30 and >60 age groups.
  • Education Impact: Borrowers with lower education show higher default risk.
  • Occupation: Laborers, core staff, and “others” drive defaults; IT staff & managers are safer segments.
  • Loan Size: Most loans fall within ₹100K–₹500K range, also where defaults concentrate.
  • Previous Credit: ₹121B in prior credit with 90.46% on-time repayment rate.
  • Loan Purpose: Mobile & consumer durables show higher risk of defaults.

🚀 Recommendations

  • Tighten credit checks for younger (<30) and older (>60) borrowers.
  • Implement financial literacy programs for low-education applicants.
  • Reassess risk pricing for loans tied to high-default categories (e.g., mobile, furniture, consumer durables).
  • Incentivize timely repayments via cashback or lower interest for consistent payers.
  • Monitor outliers with unusually high annuity-to-credit ratios.
  • Leverage unused credit capacity (37.9% utilization) for cross-selling to reliable customers.

📈 Business Impact

✅ Reduced default risks through targeted risk assessment.
✅ Improved profitability by focusing on reliable borrower segments.
✅ Smarter product strategy with data-driven insights.
✅ Stronger portfolio stability and growth opportunities.


🔗 Connect with me: LinkedIn | GitHub

About

This project offers a 360° borrower analysis using Tableau dashboards on 3+ lakh loan applications (₹184B exposure). It identifies default hotspots by age, education, occupation, and loan type, with an 8.07% default rate. Insights help institutions reduce risk, boost profitability, and make smarter loan approvals.

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