Uncovering Unknown Unknowns in Financial Services Big Data by Unsupervised Methodologies: Present and Future trends

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Gil Shabat, David Segev, Amir Averbuch ;
Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance, PMLR 71:8-19, 2018.

Abstract

Currently, unknown unknowns in high dimensional big data environments can go unnoticed for a long period of time. The failure to detect anomalies in critical infrastructure data can result in extensive financial, operational, reputational and life threatening consequences. In this paper, we describe algorithms for an automatic and unsupervised anomaly detection that do not necessitate domain expertise, signatures, rules, patterns or semantics understanding of the features. We propose several new methodologies for anomaly detection to protect critical infrastructures, with emphasis on finance, spanning from theory to actionable technology. Although anomalies can originate from several sources, we also show that cyber threat, financial and operational malfunction are converging into a single detection paradigm. Performance comparison between different algorithms (ours and others) is presented as well as examples from real use cases.

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