With all the growing number of financial transactions and lending activities carried out digitally, there is a minimal need for precision and adapting approaches in loan defaults prediction and fraud detection. This study aims to compare and contrast the traditional statistical tools and Machine-learning tools for financial risk assessment. The review focuses on the following techniques: expert-based lending practices, credit scorecards, discriminant analysis, logistic regression, rule-based fraud detection, and advanced machine-learning techniques such as decision trees, random forests, support vector machines, artificial neural networks as well as ensemble learning. Because they are intuitive, easily understood, legitimated, and user-friendly, traditional methods are still being widely practiced. They, however, rely on specific assumptions, linearity and reliance on manual feature engineering, which hampers the ability to model complex borrower behavior as well as changes in fraud patterns. Machine-learning methods offer more advanced features that encompass the ability to detect nonlinear relationships, handle big and complex financial information, and reveal underlying patterns in the risks. However, they are only effective if they can be correctly evaluated, are explainable, have quality data and represent the features. The study underscores the importance of considering other dimensions beyond predictive accuracy in the assessment of financial-risk models, such as calibration, robustness, fairness, model interpretability and economic impact. The review finds that hybrid approaches combining the seemingly clear statistical models with predictive predictive power of the machine-learning algorithms exist and look promising in reliable and sustainable credit risk management and fraud detection.