Études de cas
J.S. Held consolide son expertise en matière de litiges financiers dans le domaine du droit de la famille grâce à l'acquisition de CMM, LLP
En savoir plusA digital money transfer platform implemented a machine-learning-based fraud detection model within its transaction monitoring framework to identify potentially fraudulent activity in near real time. The model operated at scale across high-volume, customer-initiated transactions and played a critical role in influencing alert generation, investigative workflows, and downstream risk decisions.
Given the model’s material impact on customer outcomes and financial crime risk management, the platform sought independent assurance over the model’s performance, reliability, and fairness, particularly in light of increasing regulatory focus on explainability, bias, and model governance for advanced analytics.
Key Risks
Significance
As part of an independent model validation engagement, J.S. Held technical experts contributed to an independent assessment of the model's operational behavior, consistency of outputs, and potential disparate impact considerations. Based on information available at the time of review, our experts:
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