The integration of crypto rails into traditional finance is accelerating, bringing new AML, sanctions, and data-governance challenges. As stablecoins and blockchain-based payments become mainstream, traditional monitoring fails to detect cross-rail risks. Network analytics offers a scalable, privacy-aligned way to understand illicit behaviour across fiat and crypto, enabling institutions and CASPs to adapt to a multi-rail future.
Money laundering doesn’t happen in a vacuum — it hides behind other crimes. With the EU tying ESG offences directly into AML rules, these once-separate compliance worlds are converging fast. This article explores how firms can move beyond box-ticking by linking ESG and AML through value-chain data. From batteries to furniture, supply chains reveal risks that impact both sustainability disclosures and financial-crime exposure. Our open-source methodology shows how to trace those risks and translate them into clear, explainable scores, and starting points for risk-based follow up with customers and suppliers.
The EU’s Anti-Money Laundering Regulation (AMLR) and the creation of the new Anti-Money Laundering Authority (AMLA) mark the largest overhaul of Europe’s financial crime compliance regime in two decades. For executives in banks, payment institutions and crypto-asset service providers (CASPs), the shift is not to be underestimated.
In this article, we call out a number of areas where the AMLR brings material change and which have been under-reported by the myriad other writers on this topic.
Open-source data can provide valuable insights into counterparty financial economic crime (FEC) risks, complementing traditional methods for identifying FEC exposures. By analyzing company registries, sanctions lists, and geographic data, institutions can detect key risk indicators, such as shell companies and indirect links to sanctioned entities. This approach enhances the ability to uncover hidden vulnerabilities in client interactions, helping to mitigate evolving FEC threats.
Adherence to AML regulations within the EU has ensured that financial institutions generate a constant stream of financial crime risk signals from various processes (e.g. Transaction Monitoring, client due diligence reviews). Contrary to industry standard black box models, Explainable predictive models can help to streamline the operational processes to address these risk signals by allowing insight into individual case risks, based on historical information.
We tested the use of some Large-Language models (Artificial Intelligence) in the Financial Economic Crime context We found that given the requirements on AI processing power, the trial-and-error nature of prompt engineering for LLMs, and results from this analysis showing false-negative conclusions are very feasible with LLM outputs, companies facing AML, Sanctions and KYC challenges should think critically whether LLM usage in production processes is the best fit, or if tailored other AI solutions may provide better results.