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Insights

Counterparty FEC risks: open data, sanctions, and shell companies

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.

Explainable AI: unlocking value in FEC operations

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.

Benchmarking AI solutions for FEC challenges

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.