Predicting credit card approval is a classical usecase in finance industry. Analyzing the credit card applications data for a handful of applications can be done manually but if credit institutions get a large number of requests then analyzing them manually is not feasible. The applications are going to increase with growing number of digital devices and overall digital transformation across the globe. Credit card companies would need a program to analyze the historical patterns of the data and understand the impacting factors in an application and decide whether to approve the application or not based on the risk score of the applicant.
Credit Analysis involves the statistical and qualitative measure to analyze the probability of a customer to pay back the loan to the bank in time and predict its default characteristic. Analysis focus on identifying and reducing the financial risks involved which may otherwise results in the losses incurred by the company while lending. The loss of business risk will also be there by not approving the application of eligible candidate. So, it is important to manage credit risk and
handle challenges efficiently for credit decision as it can have adverse effects on credit management. Hence, assessing credit approval is significant before granting decision on each application.
In this project, I wrote code to read the data for existing approval/rejection patterns of the credit card applications and build a ML algorithm to predict the approval status for future credit card applications. I used predictive analytics and machine learning algorithms like logistic regression to predict whether the application is approved or not.
Research Questions
Below are my research questions for this project:
• What are attributes available in credit card applications data. Would there be any optional attributes for which the data would be missing
• What attributes are influencing the approval status the most.
• Are there any attributes negatively or positively moving with the approval status variable.
• Look at several data visualizations to understand the underlying data
Check out the docs folder for documentation on the project.