Machine learning in banking enhances fraud detection, risk scoring, customer segmentation, and credit decisioning. Machine learning in banking uses supervised and unsupervised models to detect anomalies, analyze spending patterns, and automate compliance checks. ML-driven systems can reduce fraud losses by up to 25% and improve loan approval accuracy significantly. Key differentiators include real-time scoring, scalable data pipelines, and strong model governance for regulatory compliance. Use cases extend to chatbots, portfolio optimization, and personalized financial product recommendations. Leading solution providers ensure data security, high model accuracy, and seamless integration with core banking systems. Debut Infotech is among the companies contributing ML solutions for modern banking environments.