Anomaly Detection in High-Dimensional Bank Account Balances via Robust Methods

TL;DR

This paper introduces and empirically evaluates robust statistical methods for detecting anomalies in high-dimensional bank account balance data, focusing on efficiency and accuracy in large-scale datasets.

Contribution

It proposes robust approaches that are computationally efficient and effective for high-dimensional anomaly detection in financial data.

Findings

Methods achieve high breakdown points

Approaches are computationally efficient

Successfully applied to 2.6 million records

Abstract

Detecting point anomalies in bank account balances is essential for financial institutions, as it enables the identification of potential fraud, operational issues, or other irregularities. Robust statistics is useful for flagging outliers and for providing estimates of the data distribution parameters that are not affected by contaminated observations. However, such a strategy is often less efficient and computationally expensive under high dimensional setting. In this paper, we propose and evaluate empirically several robust approaches that may be computationally efficient in medium and high dimensional datasets, with high breakdown points and low computational time. Our application deals with around 2.6 million daily records of anonymous users' bank account balances.