This projectfocuses on analyzing and managing risks associated with lending by leveraging historical credit data to predict borrower defaults. The project begins with defining the problem, which typically involves identifying factors that contribute to loan defaults and developing predictive models to assess credit risk. Data collection includes gathering loan data, borrower demographics, and macroeconomic factors, followed by cleaning and preprocessing the data to handle missing values, outliers, and irrelevant features.
Exploratory data analysis (EDA) is conducted to uncover patterns, correlations, and trends, often through visualizations and statistical summaries.The core of the project is data modeling, where machine learning algorithms like logistic regression, decision trees, or random forests are applied to predict defaults based on historical data.
With rising economic uncertainties, the ability to predict and manage credit landing risk has become crucial for financial institutions, including banks, credit unions, and other lenders. By minimizing this risk, institutions can enhance profitability, reduce losses, and foster trust with borrowers.