The use of ML methods in financial risk prediction is transforming the way banks and FinTech companies handle credit, fraud and their market dangers. We explore if Logistic Regression, Random Forest, Support Vector Machine (SVM) and Gradient Boosting are effective in correctly predicting financial risks within the digital finance ecosystem. Employing a properly formatted set of credit-related variables, the models were checked and rated using important metrics. Among the algorithms examined, Gradient Boosting proved to be the most successful, with accuracy at 94.2%, precision at 92.8% and recall and F1-score of 93.5%. The outcomes achieve better results than typical risk models, proving ML models suit banking applications. The report continues to discuss how FinTech and intelligent tools are continuing to impact banking, improve how decisions are made and ensure compliance with laws. The research shows that, based on relevant findings and recent studies, ML both raises accuracy and provides solutions that work well in today’s financial world. The paper highlights that for AI to be adopted in the industry over time, data needs to be accurate, models need to be transparent and AI must be ethical
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