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Engineering and Technology Book OPEN ACCESS

Next-Generation AI Systems for AML Compliance and Financial Risk Detection

Altynai Myrzabekova
Master's student in Computer Science and Engineering, University of Fairfax (USA); Specialist in Anti-Money Laundering (AML) and financial risk analysis (based on previous professional experience); Founder of a project developing an AI platform for detecting financial crimes Arlington, Virginia, USA
tajet 2026
JOURNAL PUBLICATION JANUARY
FULL TEXT PP. 01-51
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VOLUME —
ISSUE —
YEAR 2026
PAGES 01-51
RESEARCH OVERVIEW

Abstract

The rapid digitization of financial systems has intensified both the volume of transactions and the complexity of financial crime schemes, rendering traditional rule-based anti-money laundering (AML) systems inadequate for contemporary compliance demands. This methodological guide examines how artificial intelligence and machine learning technologies can transform financial monitoring processes, improve detection accuracy, and reduce operational burdens on compliance professionals. The study analyzes the normative-legal framework governing AML compliance in the United States, including requirements set by the Bank Secrecy Act, the Financial Crimes Enforcement Network (FinCEN), the Office of Foreign Assets Control (OFAC), and the Financial Action Task Force (FATF). Drawing on practical banking experience and current academic research, the author develops and systematizes an original five-stage AI-oriented AML methodology that covers the full monitoring cycle: multi-source data integration, data preparation and entity resolution, intelligent behavioral and network analysis, dynamic risk scoring, and explainable decision support with regulatory reporting. The author also proposes a unified SaaS platform framework that integrates behavioral analytics, anomaly detection, KYC/CDD/EDD automation, explainable AI, case management, and regulatory reporting within a single AML compliance ecosystem. Unlike traditional rule-based AML systems, which rely mainly on fixed thresholds and static scenarios, the proposed framework is designed to support adaptive risk assessment, continuous model learning, and transparent human-in-the-loop compliance decisions. Published industry benchmarks indicate that machine-learning-enabled AML tools can materially reduce false positives and improve the quality of alerts; therefore, the methodology is positioned as a practical next-generation approach for compliance officers, fintech developers, risk analysts, and regulators seeking scalable AML modernization.

RESEARCH TOPICS

Keywords

Anti-money laundering Aml compliance Artificial intelligence in finance Machine learning Financial crime detection Behavioral analytics Explainable ai Kyc/cdd/edd Risk scoring Fincen regulation Fatf standards Financial monitoring platform

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