AI-Driven Risk Management in Banking: Enhancing Credit Scoring andFraud Detection through Machine Learning

Abstract: The accelerated emergence of machine learning in the banking industry has changed the way traditional risk is managed, allowing financial organisations to assess and deal with risks more precisely than ever before. This research “AI-Enabled Risk Management in Banking: Disrupting Credit Scoring and Fraud Detection using Machine Learning” highlights how AI models can do a better job than traditional rule driven systems when it comes to predicting creditworthiness and identifying fraudulent behavior. By utilizing techniques like supervised and unsupervised learning—such as logistic regression, random forest, gradient boosting, neural networks—banks can analyze large amounts of data in real time to surface hidden patterns and anomalies that contribute to risk. It suggests that fairer and more balanced lending decisions could be made by including alternative data sources from social behaviours to transaction histories, to mobile usage when assessing loan applications. And also investigates the effectiveness of anomaly detection, NLP (natural language processing) and graph-based algorithms in detecting fraud patterns with higher accuracy. The report shows that AI solutions improve not only predictive performance and operational efficiency, but also regulatory compliance and customer confidence. Nevertheless, issues in terms of data privacy, model interpretability and algorithmic bias still persist. The paper ends with calling for a governance model on the responsible AI adoption to ensure transparency and ethical accountability in banking industry’s digital ecosystem evolution.

Keywords: AI-Enabled Risk Management, Credit Scoring Models, Fraud Detection Algorithms, Machine Learning in Banking, Responsible and Ethical AI.