The cost and complexity of financial crime compliance (FCC) continue to rise, often without measurable improvements in effectiveness. While AI offers potential, most solutions remain opaque and poorly aligned with regulatory expectations. This paper presents the design and deployment of an agentic AI system for FCC in digitally native financial platforms. Developed through an Action Design Research (ADR) process with a fintech firm and regulatory stakeholders, the system automates onboarding, monitoring, investigation, and reporting, emphasizing explainability, traceability, and compliance-by-design. Using artifact-centric modeling, it assigns clearly bounded roles to autonomous agents and enables task-specific model routing and audit logging. The contribution includes a reference architecture, a real-world prototype, and insights into how Agentic AI can reconfigure FCC workflows under regulatory constraints. Our findings extend IS literature on AI-enabled compliance by demonstrating how automation, when embedded within accountable governance structures, can support transparency and institutional trust in high-stakes, regulated environments.

Banks hold a societal responsibility and regulatory requirements to mitigate the risk of financial crimes. Risk mitigation primarily happens through monitoring customer activity through Transaction Monitoring (TM). Recently, Machine Learning (ML) has been proposed to identify suspicious customer behavior, which raises complex socio-technical implications around trust and explainability of ML models and their outputs. However, little research is available due to its sensitivity. We aim to fill this gap by presenting empirical research exploring how ML supported automation and augmentation affects the TM process and stakeholders' requirements for building eXplainable Artificial Intelligence (xAI). Our study finds that xAI requirements depend on the liable party in the TM process which changes depending on augmentation or automation of TM. Context-relatable explanations can provide much-needed support for auditing and may diminish bias in the investigator's judgement. These results suggest a use case-specific approach for xAI to adequately foster the adoption of ML in TM.

The volume of money laundering and other financial crimes is growing worldwide--and the techniques used to evade their detection are becoming ever more sophisticated. This has elicited a vigorous response from banks, which, collectively, are investing billions each year to improve their defenses against financial crime (in 2020, institutions spent an estimated $214 billion on financial-crime compliance). 1 1. What's more, the resulting regulatory fines related to compliance are surging year over year as regulator's impose tougher penalties. But banks' traditional rule- and scenario-based approaches to fighting financial crimes has always seemed a step behind the bad guys, making the fight against money laundering an ongoing challenge for compliance, monitoring, and risk organizations. Now, there is an opportunity for banks to get out in front.

Your challenge is to improve transaction monitoring through data and decrease fraud attempts that affect our users. This is your chance to help our compliance team catch the bad guys and protect our user base. Do you have what it takes to make a difference? Then test your bunq fit to start your application. For you to be successful as a Data Science Intern - Transaction Monitoring, you need a couple of hard skills.

Yesterday I was approached multiple times about this FD article and Sygno's experience with regulators and Machine Learning in Transaction Monitoring. And, due to the lawsuit, with special interest in how the Dutch central bank (DNB) operates in these matters. Though I don't know the details of this case other than those presented in the media, it seems we have a vastly different experience with (Dutch) regulators. We see a regulator that actively promotes Machine Learning and usage of Data. That doesn't mean that all Machine Learning initiatives pass their scrutiny.

There is an increasing demand for financial crime mitigation and regulatory compliance solutions that take care of the future demands of the financial services industry. Adapting the AML processes and platforms to combat the changing ways of criminal activities has become even more critical as financial crimes grow using banking channels. Given the need for disruptive technological enhancements, the global regulatory bodies are recommending automation enhanced by machine learning. In the latest report by FATF that outlines the improvements financial institutions must make in their respective jurisdictions, it is evident that they have paid close attention to transaction monitoring. Since global digital transaction volumes have grown so much in the last decade, transaction monitoring costs are dramatically increasing as well. This also burdens compliance officers since investigating the number of suspicious transactions takes a toll.

Asset managers today are faced with an overwhelming quantity of data. The paradox for many managers is that as the avalanche grows, clients and regulators are increasingly keen to look at the data on a much more granular level than in the past. Faced with these dual challenges and competing priorities, institutions are increasingly turning to data service experts to help them cope. Some managers turn to external third-party providers to ensure that they maintain oversight and control of their data in a transparent manner. And asset servicers, who are already close to the business and know the ins and outs, can perform many of these functions, replacing outdated operational processes and spreadsheets with more fit for purpose digital solutions.

Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people. These nefarious industries rely on sophisticated money laundering schemes to operate. Despite tremendous resources dedicated to anti-money laundering (AML) only a tiny fraction of illicit activity is prevented. The research community can help. In this brief paper, we map the structural and behavioral dynamics driving the technical challenge. We review AML methods, current and emergent. We provide a first look at scalable graph convolutional neural networks for forensic analysis of financial data, which is massive, dense, and dynamic. We report preliminary experimental results using a large synthetic graph (1M nodes, 9M edges) generated by a data simulator we created called AMLSim. We consider opportunities for high performance efficiency, in terms of computation and memory, and we share results from a simple graph compression experiment. Our results support our working hypothesis that graph deep learning for AML bears great promise in the fight against criminal financial activity.

Within the financial services sector, Anti-Money Laundering (AML) is a significant challenge for many institutions, often consuming large numbers of people and effort to manage the process and comply with the regulations. As a result, these same institutions are looking for new solutions to help them reduce the burden and increase the controls in this complex space. The combination of artificial intelligence (AI) and, more specifically, machine learning (ML), are increasingly being considered as enablers of a better solution. Despite its potential, however, adoption of AI and ML within Anti-Money Laundering has been relatively slow. This is due, in part, to the limited understanding of how AI and ML could be applied within compliance programs, and to the fact that regulators and compliance officers are often concerned that AI and ML are "black boxes" whose inner workings are not clearly understood.

This is a key question at the heart of efforts to tackle money laundering: if you work for a bank or other financial institution and have suspicions money laundering is happening, you have a legal duty to speak up. Your suspicion could be based on pure intuition – a sense that something just doesn't quite add up – but the law nonetheless expects you to act. Across a huge institution, however, processing millions of transactions per hour, only a tiny share of customers will meet an actual human being. Could software be taught the human intuition that senses something is not quite right? Or can it pick out suspicious behaviour that even humans might not notice?