ENHANCING CREDIT CARD APPROVAL ACCURACY USING MACHINE LEARNING MODEL

In the data-driven financial world of today, organizations need smart systems that can facilitate documentation-based decision-making. This paper describes a machine learning–based framework for improving the accuracy of credit card approval decisions based on structured data. By using a combination of supervised learning algorithms such as Random Forest, Logistic Regression, and Naive Bayes, the proposed model analyzes applicant information to facilitate automated classification and risk documentation. The research employs a public financial dataset and incorporates a Streamlit-based GUI to mimic a decision support setting for bank officials and analysts. The system not only enhances classification precision but also helps with effective information retrieval and knowledge structuring in credit risk management processes. This study emphasizes how decision systems based on AI can help minimize documentation overheads manually and deliver uniform, data-driven approval decisions, thus serving the larger area of intelligent financial information systems.