With digital banking becoming not only operationally dynamic but also hectic, there is a proliferation of transaction volumes and speeds, corresponding to which the sophistication of financial crime has also rapidly escalated. The mainstream fraud detection methods are ineffective, mainly due to their rule-based nature, and cannot adapt to the dynamic nature of day to day frauds, especially in real time. In this regard, the purpose of this chapter is to ensure a paradigm shift by developing AI-based monitoring systems for transactions, which themselves are capable of ingesting, analysing, and responding to any anomalies in high-speed transactional data. Using advanced machine learning models like LSTM, GRU, and the transformer architecture, along with various unsupervised anomaly detection methods—such as Isolation Forests, Autoencoders, One-Class SVM etc.—the system detects unusual transaction behaviour attributable to account takeover, money laundering, phishing and synthetic fraud. In addition, we are investigating the feasibility of streaming data pipelines and online learning techniques to allow real-time responsiveness and adaptability of the models. We describe several feature-engineering methodologies concerned with capturing temporal and behavioural signals in conjunction with context-based risk profiling frameworks. The paper focuses on contemporary AI systems, Fintech use cases to measure off-the-shelf (COTS) software and tools. The section studies regulatory compliance and model explain-ability (XAI) along with a cost–benefit analysis of reducing false positives. Finally, the section proposes a scalable, intelligent and interpretable fraud detection paradigm in digital banking that would help data scientists, financial regulators as well as banking technology architects.