SELECTED R&D REPORTS IN COMPLIANCE

This collaborative research with Akbank introduces a groundbreaking methodology for anti-money laundering (AML) using time-frequency analysis to enhance the detection of suspicious transactions. By combining AI-driven techniques with real banking data, this approach significantly reduces false positives, streamlines compliance operations, and strengthens transaction monitoring accuracy. Learn how this innovative solution transforms AML compliance through advanced feature engineering and model optimization.

This forward-looking report, developed with insights from global AML/CFT experts, envisions how financial crime prevention will evolve over the next decade. Exploring future scenarios and trends, it highlights the role of advanced technologies such as AI, data sharing frameworks, and evolving compliance strategies. Discover how compliance teams can adapt, mitigate emerging risks, and capitalize on technological advancements to strengthen global financial security.

H3M Analytics is proud to support global efforts against predicate offenses fueling financial crimes. Each year, we pledge 2.5% of our EBITDA to a carefully selected list of non-profit organizations that combat corruption, human trafficking, and more. Our initiative reflects our core belief in moving beyond compliance to create lasting change through technology and community partnerships. Together, we strive to dismantle the root causes of financial crime and foster a world grounded in justice.

This comprehensive MS Thesis from Boğaziçi University, supported by H3M Analytics, focuses on developing scalable models to detect suspicious activities within cryptocurrency networks like Bitcoin and Ethereum. 

Using advanced machine learning techniques such as XGBoost and Random Forest, the study showcases effective methods for identifying illicit wallets and analyzing complex transaction networks, contributing to enhanced financial crime prevention in the crypto space.

This cutting-edge research, conducted in collaboration with the University of Ottawa and supported by H3M Analytics, explores the application of active learning (AL) and semi-supervised learning (SSL) techniques for detecting malware threats. By leveraging Random Forest and XGBoost algorithms, the study highlights how AL and SSL strategies can significantly enhance detection accuracy, even with limited labeled data. This work sets a new benchmark in cybersecurity by reducing manual labeling efforts while improving predictive performance, contributing to a more robust defense against evolving threats.

This pioneering master's thesis, supported by H3M Analytics in collaboration with Boğaziçi University, delves into network-based methodologies to detect and combat money laundering activities. Leveraging advanced machine learning algorithms and comprehensive network analysis, the research aims to reduce false positives and improve detection accuracy. By integrating transactional network data, the study contributes to building scalable models for a more robust and efficient anti-money laundering (AML) system, strengthening compliance efforts in the financial sector.