Mr-Akumani/loan-default-analysis-ghanaloanconnect

Data analysis and machine learning project to predict and reduce loan defaults in Ghana's lending sector # Loan-Default-Analysis-Ghanaloanconnect Data analysis and machine learning project to predict and reduce loan defaults in Ghana's lending sector # Loan Default Analysis: Ghana Lending Sector ## 📋 Project Overview This data analytics project addresses the critical challenge of loan defaults in Ghana's non-bank financial institution (NBFI) sector. GhanaLoanConnect, a peer-to-peer lending platform, faces a 16.01% default rate—significantly above industry standards—threatening its sustainability and financial inclusion mission. ## 🎯 Business Objectives 1. **Identify High-Risk Segments**: Pinpoint sectors and borrower profiles with the highest default probability 2. **Diagnose Root Causes**: Analyze factors driving repayment difficulties 3. **Develop Mitigation Strategies**: Recommend data-driven interventions to reduce defaults by 20% ## 💡 Key Insights & Impact Analysis of 9,578 loan records revealed critical risk patterns: * **Small Business loans** showed the highest vulnerability with a **28% default rate**, followed by Educational loans at **20%** * **Credit scores** demonstrated strong predictive power - borrowers with FICO 600-649 had a **32% default rate** vs **7%** for 750+ * **Interest rate correlation** was evident - loans >15% interest had **24.8% default rate** vs **4.2%** for rates <8% * **Machine learning model** achieved **84% accuracy** in predicting high-risk borrowers ## 🚀 Recommendations & Implementation A phased strategy was developed to transform risk management: 1. **Risk-Based Pricing:** Implement tiered interest rates (8%-18%) based on borrower risk profiles 2. **Sector-Specific Underwriting:** Apply enhanced due diligence for Small Business and Educational loans 3. **Automated Decision Systems:** Integrate ML model to flag high-risk applicants and reduce processing time by 65% 4. **Portfolio Diversification:** Rebalance toward lower-risk segments (Major Purchases: 11% default, Credit Cards: 12%) ## 📈 Expected Business …