/Financial-Risk-Detection

This project uses EDA and machine learning to predict loan defaults, improving risk assessment for a finance company. Insights from historical loan data inform decision-making to minimize financial losses and ensure fair lending practices.

Primary LanguageJupyter Notebook

Financial Risk Detection

Overview

This project aims to leverage Exploratory Data Analysis (EDA) and machine learning to conduct risk analysis for loan default prediction in the context of a consumer finance company. By analyzing historical loan application data, we identify patterns and factors that indicate whether a client is likely to default on their loan payments. This analysis assists the company in minimizing financial losses while ensuring that creditworthy applicants are not unfairly rejected.

Problem Statement

The lending industry faces significant challenges in assessing creditworthiness, particularly for applicants with limited or no credit history. Loan defaults pose financial risks to lending institutions, making accurate risk assessment crucial. Our primary objective is to use EDA and machine learning techniques to understand the drivers behind loan default and develop strategies to mitigate these risks effectively.

Key Tasks

  1. Data Cleaning: Preprocess the loan application data to handle missing values, outliers, and ensure data consistency.
  2. Exploratory Data Analysis (EDA): Analyze the dataset to identify patterns and trends related to loan default.
  3. Machine Learning Model Building: Develop predictive models using various machine learning algorithms to predict loan default.
  4. Model Evaluation: Evaluate the performance of the machine learning models using appropriate metrics and techniques.
  5. Portfolio Management and Risk Assessment: Utilize insights gained from the analysis to inform portfolio management, risk assessment, and lending practices.

Tools Used

  • Python: Pandas, NumPy, Scikit-learn
  • Jupyter Notebook: For data exploration, analysis, and model building
  • GitHub: For version control and collaboration
  • Machine Learning Libraries: XGBoost, RandomForest, Logistic Regression

Conclusion

Through this project, we have successfully leveraged EDA and machine learning techniques to analyze historical loan application data and predict loan default. By identifying key factors influencing loan default and developing predictive models, we can assist the lending company in minimizing financial losses and optimizing lending decisions. Insights gained from the analysis can be used to improve portfolio management, risk assessment, and lending practices, ultimately contributing to a more robust and efficient lending process.