The primary objective of this study is to harness the power of machine learning to enhance credit risk assessment accuracy. To achieve this goal, we have employed two distinct learning algorithms, each bringing its unique strengths to the table. By leveraging the capabilities of these algorithms, we aim to develop a more robust and adaptable credit scoring model that can effectively analyze a wide array of borrower attributes and transaction histories.