With the rapid integration of digital banking in the United States, financial markets are beginning to experience difficulty in maintaining secure, efficient, and ethically viable methods of fraud detection and customer verification. This paper explores the convergence of AI-based fraud identification algorithms and biometrics Know Your Customer (KYC) systems, including face recognition and fingerprinting, within the American online banking industry. Based on a combination of publicly available and synthetic transaction sets, we utilize machine learning-based models, such as XGBoost, Isolation Forest, and LSTM, to identify anomalous financial behaviors in real-time. At the same time, we evaluate whether biometric KYC tools will help reduce onboarding fraud and identity theft. Ethical and legal considerations are tackled through explainer tools like SHAP and LIME, which make the decision process in the models more transparent. As the outcomes demonstrate, AI-optimized systems enhance the accuracy and speed of fraud detection and hasten the audit processes, in addition to facilitating adherence to the regulatory requirements, such as the Bank Secrecy Act (BSA) and the GDPR. This article adds a lens of ethically aligned innovation to digital banking, and actionable intelligence to regulation technology (RegTech), compliance auditing, and the FinTech sector as a whole.