From KYC to KYCB: Moving Towards Behavioral Compliance in Digital Finance

From KYC to KYCB: Moving Towards Behavioral Compliance in Digital Finance Author: Rohit Rajdev Affiliation: Sandscript AI Contact: [email protected] Date: October 2025 Abstract Traditional Know Your Customer (KYC) models tend to focus on verification of identity at specific timestamps, but are not as adept at observing slowly changing or sophisticated patterns of payment fraud that arise post-onboarding. In this work, we propose the KYCB framework as an adjunct layer to conventional KYC and transaction monitoring mechanisms. KYCB focuses on near-real-time analysis of behavioral signals—transaction flows, device fingerprints, biometrics, and relational graphs—to uncover anomalies and increase compliance durability. This paper presents the conceptual model, followed by a literature review; benefits, challenges in implementation, and future directions for policy and research are discussed. Keywords: KYC; KYCB; Behavioral Biometrics; Fraud Detection; AML; RegTech; Compliance 1. Introduction Know Your Customer (KYC) has been the lynchpin of anti-money laundering (AML) and counter-fraud compliance. But its static verification denies these dynamically evolving threats after merchant onboarding. Deepfakes, synthetic identity, and account takeovers have opened up this vulnerability. The key contribution of this paper is Know Your Customer’s Behavior (KYCB) as an adaptive compliance model to complement KYC by continuously evaluating customer behavior and activity patterns. 2. Literature Review Research indicates that behavioral analytics improves fraud detection above and beyond static identity verification. Yousefi et al. (2019) reviewed machine learning for credit card fraud detection and highlighted the significance of dynamic feature monitoring. Abuhamad et al. (2020) investigated continuous authentication for behavioral biometrics, while Salami et al. (2025) presented AI-oriented behavior analysis in digital banking. Collectively, these studies all support the importance of real-time behavioral-based adherence. 3. Conceptual Framework: KYCB KYCB is another compliance layer added to the KYC and KYT (Know Your Transaction) ecosystem. It constantly analyzes transactional, behavioral, and contextual data to determine whether the customer remains trustworthy. It includes the following: (1) transaction dynamics: velocity, location, frequency; (2) device context: fingerprinting, IP address, and geolocation; (3) behavioral biometrics: typing, gesture, navigation profiling; and (4) relational analytics: network connections and mule-account mapping. These inputs are utilized to develop adaptive risk scoring and anomaly alerts. 4. Use Cases KYCB is able to catch sophisticated fraud where a static check would miss it: • Money Mule Detection: back-and-forth transfers in specific rhythm to launder funds. • Account Takeover: behavioral biometrics identify changes in the way users typically behave. • Synthetic Identity Fraud: misalignment between behavior and verified identity. • Travel Scenarios: adaptive friction as a user acts from new geographic areas. 5. Benefits and Challenges Benefits: (1) Early detection of emerging fraud. (2) Reduced false positives. (3) Dynamic compliance posture. (4) Increased customer trust. Challenges: Data privacy under GDPR/CCPA; integration with legacy systems; model explainability; maintaining a fine line between automation and human oversight. 6. Future Work Future work will focus on the other applications of KYCB in the context of decentralized finance (DeFi), cross-border compliance, and privacy-preserving AI (federated learning, differential privacy). Regulators, financial institutions, and AI developers need to work together to establish ethical standards and governance controls. 7. Conclusion The evolution from KYC to KYCB represents a seismic shift in financial compliance—from a process of static verification to one of continuous trust assurance. By implementing behavioral analytics and adaptive monitoring, financial services organizations can create robust systems that are well-equipped to fight next-generation fraud and achieve regulatory confidence. References Abuhamad, M., Abusnaina, A., Nyang, D., & Mohaisen, D. (2020). Sensor-based Continuous Authentication of Smartphones’ Users Using Behavioral Biometrics: A Contemporary Survey. arXiv preprint. Edgars, M., & Benson, D. (2024). AI and Regulatory Compliance: The KYC, AML and Transaction Monitoring Issues. SSRN. Pryor, L., Mallet, J., Dave, R., Seliya, N., Vanamala, M., & Boone, E. (2022). Assessment of a Behavioral Biometrics and Machine Learning User Authentication Framework. arXiv preprint. Salami, et al. (2025). AI-Powered Behavioural Biometrics for Fraud Detection in Digital Banking. Asian Journal of Research in Computer Science. Yousefi, N., Alaghband, M., & Garibay, I. (2019). Credit Card Fraud Detection Using Machine Learning Algorithms. arXiv preprint. Wang, C., Tang, H., Zhu, H. Y., Zheng, J. H., & Jiang, C. J. (2024). Behavioral Authentication for Security and Safety. Security and Safety, 3:2024003. Turki, M., et al. (2020). The 'RegTech' and the Money Laundering Prevention. ScienceDirect / Elsevier. Harvey, J. (2020). Audit of the AML/CTF Transaction Monitoring System. ACAMS white paper.