Sleuthing for adverse outcomes: Using anomaly detection to identify unusual behaviors of third-party agents

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Michelle Miller, Robert Cezeaux ;
Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance, PMLR 71:121-125, 2018.

Abstract

Business transactions between customers and financing entities are often governed by intermediary agents. In this scenario, actions taken by these agents can a ect the likelihood of adverse outcomes for both the customers and the financial institution. Our goal is to establish a general framework that identi es these types of anomalous agents. In this paper, we demonstrate a novel application of anomaly detection using isolation forests to identify which agents may be associated with adverse outcomes. We apply a genetic algorithm to understand which features were key to the performance of anomaly detection and and suggest a general framework for problems that similarly concern the behaviors of third-party agents.

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