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Massachusetts Loan Default Risk
Project Type
EDA
Date
September 2025
Github
Description
Click the images to see the insights
Keywords
EDA, Python, Financial Analytics
Financial institutions often struggle to assess borrowers with limited credit history and local economic context is rarely factored into lending decisions. This project analyzes LendingClub's 2019 loan dataset to uncover the drivers of credit default at a micro-market level, connecting individual borrower attributes (FICO score, income, loan grade, term length) with Massachusetts economic conditions. Starting with national trends and narrowing to a Massachusetts-focused lens, the analysis reveals that loan grade, term length, and income level are strongly predictive of default risk, while MA's portfolio skews toward higher-grade, lower-risk borrowers relative to the national average. Findings offer lenders a framework for more precise risk pricing while expanding responsible credit access.















