This project, led by Deepti Singh as the Team Lead at Evoastra Ventures Pvt Ltd, focuses on credit risk analysis for a consumer finance company. The goal is to apply Exploratory Data Analysis (EDA) to optimize the company's loan application process by identifying patterns in customer data that help mitigate financial risk while enhancing customer satisfaction.
The finance company faces challenges in loan approval due to insufficient or non-existent credit histories of applicants. By analyzing previous loan applications, the project aims to ensure that capable applicants are not rejected, while minimizing the risk of lending to those likely to default.
- Improve loan approval rates while managing financial risks.
- Enhance customer satisfaction by refining the loan application process.
- Ensure compliance with regulatory standards.
- Provide actionable insights into process optimization and product strategy.
The dataset includes information about previous loan applications and the outcomes (Approved, Cancelled, Refused, Unused Offer). This data forms the basis for EDA to uncover patterns related to loan approval and customer behavior.
- Identified key factors that influence loan approval and rejection.
- Analyzed customer cancellation behavior to optimize offerings.
- Assessed risk and compliance management to safeguard financial interests.
- Python
- Pandas, NumPy
- Matplotlib, Seaborn
- Jupyter Notebook
The project provides valuable insights into the loan application process, helping the finance company make data-driven decisions that reduce financial risks, optimize operations, and improve customer satisfaction.
- data/: Contains the datasets used for the analysis.
- notebooks/: Jupyter Notebooks with EDA and findings.
- README.md: This file with the project overview.
- Team Lead: Deepti Singh
- Contributors: Evoastra Ventures Pvt Ltd Data Science Team