A Credit Card Approval Prediction System using Data Analysis and Rule-Based Model
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Abstract
Credit card approval is a critical process in financial institutions, requiring accurate evaluation of applicant details to minimize risk and ensure reliability. Traditional approval systems rely on manual verification, which is time-consuming, inconsistent, and prone to human errors. This paper presents a Credit Card Approval Prediction System that automates the decision-making process using data analysis and a rule-based model. The system collects user inputs such as age, income, credit score, work experience, and existing debt, and processes them to generate approval or rejection results. The application is developed using Python and Flask, with an interactive user interface designed using HTML and CSS. Additionally, the system provides probability values and graphical representations such as pie charts and bar charts to enhance result interpretation. The proposed system improves efficiency, reduces processing time, and ensures consistent decision-making. It also serves as a foundation for future integration with advanced machine learning techniques.
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