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

TL;DR

This paper introduces a novel choice-based, synthetic data labeling approach for inherent risk scoring, improving data efficiency and consistency in anti-money laundering applications.

Contribution

It proposes a new paradigm combining choice-based labeling with synthetic data generation to address data scarcity and inconsistency in risk scoring models.

Findings

Achieved 89% accuracy on test data

Attained 93% ROC-AUC

Demonstrated effectiveness with small synthetic dataset

Abstract

Inherent risk scoring is an important function in anti-money laundering, used for determining the riskiness of an individual during onboarding $\textit{before}$ fraudulent transactions occur. It is, however, often fraught with two challenges: (1) inconsistent notions of what constitutes as high or low risk by experts and (2) the lack of labeled data. This paper explores a new paradigm of data labeling and data collection to tackle these issues. The data labeling is choice-based; the expert does not provide an absolute risk score but merely chooses the most/least risky example out of a small choice set, which reduces inconsistency because experts make only relative judgments of risk. The data collection is synthetic; examples are crafted using optimal experimental design methods, obviating the need for real data which is often difficult to obtain due to regulatory concerns. We present the methodology of an end-to-end inherent risk scoring algorithm that we built for a large financial institution. The system was trained on a small set of synthetic data (188 examples, 24 features) whose labels are obtained via the choice-based paradigm 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%.