The Loan Default Analysis project aims to identify key factors contributing to loan defaults by analyzing borrower profiles, financial data, and credit risk indicators. Using statistical methods, visualizations, and predictive modeling, the project provides insights to mitigate risks and improve lending strategies.
The Loan Default Analysis project focuses on understanding and minimizing financial risk in lending by analyzing factors that contribute to loan defaults. The objective is to develop a structured approach to risk analytics in banking and financial services, leveraging data to make informed lending decisions.
By examining key variables such as loan type, loan purpose, commercial nature, credit scores, upfront charges, loan amounts, interest rates, and property values, the project aims to uncover significant patterns and correlations influencing default tendencies. The analysis will provide insights into high-risk borrower profiles and factors that increase the likelihood of default, helping financial institutions enhance their risk assessment models.
Through statistical analysis, exploratory data analysis (EDA), and visualization techniques, this study will identify trends, assess risk factors, and propose data-driven recommendations for mitigating loan defaults. The project is open-ended, allowing for deeper exploration, additional research, and personalized insights to strengthen lending strategies and proactive risk management.