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Inherent risk scoring is an important function in anti-money laundering, usedfor determining the riskiness of an individual during onboarding$\textit{before}$ fraudulent transactions occur. It is, however, often fraughtwith two challenges: (1) inconsistent notions of what constitutes as high orlow risk by experts and (2) the lack of labeled data. This paper explores a newparadigm of data labeling and data collection to tackle these issues. The datalabeling is choice-based; the expert does not provide an absolute risk scorebut merely chooses the most/least risky example out of a small choice set,which reduces inconsistency because experts make only relative judgments ofrisk. The data collection is synthetic; examples are crafted using optimalexperimental design methods, obviating the need for real data which is oftendifficult to obtain due to regulatory concerns. We present the methodology ofan end-to-end inherent risk scoring algorithm that we built for a largefinancial institution. The system was trained on a small set of synthetic data(188 examples, 24 features) whose labels are obtained via the choice-basedparadigm using an efficient number of expert labelers. The system achieves 89%accuracy on a test set of 52 examples, with an area under the ROC curve of 93%.

Accurate, Data-Efficient Learning from Noisy, Choice-Based Labels for Inherent Risk Scoring