At AFCLab, we believe that fighting financial crime begins with a knowledgeable and empowered workforce. Our research focuses on designing and delivering impactful training programs for investigators, compliance officers, and frontline staff. Through simulation-based learning, interactive case studies, and expert-led modules, we enable professionals to recognize red flags, apply investigative techniques, and remain compliant with evolving regulations.

We also explore digital learning tools, competency frameworks, and capacity-building strategies that ensure long-term institutional resilience against financial crime.

Modern investigations require intelligent tools that go beyond keyword search and static rule sets. In this theme, we develop virtual assistants and AI-driven systems that help investigators and analysts make faster, more informed decisions. Our systems integrate legal knowledge bases, regulatory frameworks, and contextual cues to suggest relevant acts, cases, or procedures during an investigation.

We also research how to design explainable and trustworthy AI that collaborates with human experts rather than replacing them. These tools aim to enhance productivity, consistency, and transparency in investigative workflows.

Scams evolve rapidly — and so must our response. This theme centers on mapping the tactics, techniques, and procedures (TTPs) used in modern scams, including phishing, investment fraud, romance scams, and impersonation.

We have developed a Scam Matrix, to help institutions visualize attacker behavior and response strategies. Our work includes behavioral analysis of victims and perpetrators, scam lifecycle modeling, and early-warning detection systems.

By bridging technical, psychological, and communication domains, we aim to protect individuals and institutions from manipulation-based threats.

Effective financial crime prevention is not only about catching bad actors — it's about streamlining the systems and workflows that support detection and investigation. This theme focuses on optimizing how institutions respond to financial crime, from alert generation to case closure.

We apply process mining, workflow analytics, and automation techniques to improve the efficiency, scalability, and accuracy of financial crime operations. This includes work on Suspicious Activity Report (SAR) automation, AML triage optimization, and human-in-the-loop investigation systems.
Our goal is to reduce operational burden while maintaining high compliance standards.

Financial crime is increasingly driven by cyber capabilities — botnets, malware, crypto theft, and dark web activity all play a role. In this theme, we investigate how cybercriminals operate and how to disrupt them.

Our work includes forensic analysis of crypto-based money laundering, monitoring of threat actor infrastructures, and detection of scams at scale using machine learning. We also collaborate with cybersecurity researchers to build robust financial systems that are resilient to fraud, breach, and manipulation.
This theme connects financial intelligence with cyber threat intelligence for a holistic defense.

AI has immense potential in financial crime prevention — but it must be used responsibly. This theme investigates how to build fair, transparent, and accountable systems that institutions and the public can trust.

We develop techniques for explainable AI, bias detection, and privacy-preserving learning in environments where sensitive data and high-stakes decisions are the norm. We also explore regulatory implications and compliance-by-design strategies for AI systems.

This research ensures that technological advancements align with ethical standards and legal frameworks.