AI-enhanced adaptive two-factor authentication mechanisms for secure and frictionless financial services

Traditional Static Multi-Factor Authentication is no longer sufficient toprotect against the growing sophistication of cyber threats such as AI-Generated Deepfakes and Automated Credential Stuffing. Furthermore, the rigid nature of traditional authentication protocols creates significant user friction, which leads to High-Transaction abandonment rates. This paper examines an Artificial Intelligence (AI)-Based Adaptive Two-Factor Authentication (A2FA) or Risk-Based Authentication (RBA) system that uses Machine Learning (ML), behavioral biometrics and Real-Time contextual analysis to dynamically change security friction based upon a calculated risk score. This paper presents a comprehensive architectural framework, review recent empirical advancements such as Dual-Agent Long-Term Memory (LTM) and Short-Term Memory (STM) configurations, and evaluates the tradeoff between Robust Security (Zero Trust) and seamless user experience. Ultimately, the research will demonstrate that AI-based adaptive mechanisms can reduce false rejection rates while maintaining Sub-Millisecond processing latency, thus creating secure and frictionless digital banking environments.