During the last 3 months of a year, or Q4, the amount of spending tends to increase. In today's financial landscape, accurately forecasting customer spending and managing credit exposure is key to both customer satisfaction and organizational growth. Our project develops a predictive model to forecast Q4 2025 spending. We go on to categorize accounts and identify accounts eligible for credit line increases (CLI), and recommend credit line adjustments with factors such as financial risk and behavior in mind.
- Build a predictive model to forecast a customer's spending for Q4 based on past years' spending trends and recent last 8 months of the current year.
- Build a classification model to segment the customers based on the account data.
- Identify risk of overextension, fraud, or potential defaults.
- Build a model that help identify a credit limit adjustments based on spending patterns and risk factors
- Video Presentation: https://youtu.be/jY3PwWgBiOM
- Jupyter Notebooks (Code):
Jupyter Notebook Code - Regression and CLI.ipynb
- Shubhan Chari, Yashvi Sanam, Anjali Penmutcha, and Naman Behl
This project is for educational and internal research use only.