This project focuses on predicting credit risk using Long Short-Term Memory (LSTM), a variant of Recurrent Neural Networks (RNNs) optimized for financial sequential data. By capturing long-term temporal dependencies in credit history and financial behavior, the system identifies high-risk loan debt with significantly higher accuracy than traditional linear models. The analysis incorporates business-critical metrics such as Default Capture Rate and Approval Rate to maximize institutional profitability.