LoanTap Credit Risk Prediction

A Regression Model to predict loan repayment outcomes and assess the creditworthiness of borrowers based on their employment and financial behavior.

• Predicted loan repayment outcomes using customer employment and financial behavior data.
• Developed a Logistic Regression model leveraging data such as income level, employment status, and past loan repayment history.
• Delivered actionable insights that enhanced risk assessment strategies for loan approvals.

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Problem Statement

• Primary Goal: Predict whether a credit line should be extended to an individual (Binary Classification: "Yes" or "No").
• Secondary Goals:
  • Provide recommendations on repayment terms for approved credit lines.
  • Suggest actions to reduce risk and improve repayment rates.

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Key Insights

• Identified high-risk borrowers using features such as loan amount, interest rate, DTI (Debt-to-Income) ratio, and annual income.
• Discovered trends, including that longer-term loans have a higher probability of being charged-off.
• Recommended adjustments to pricing and underwriting standards to enhance risk mitigation strategies.

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Data and Preprocessing

• Dataset: Includes numerical and categorical features such as loan amount, interest rate, employment title, and loan purpose.
• Preprocessing Steps:
  • Handled missing data with imputation.
  • Cleaned outliers using robust IQR-based clipping.
  • Encoded categorical variables and performed feature reduction using PCA.