Unsupervised Financial Fraud Detection Using Low-rank Recovery

Abstract

Many outlier detection methods have been studied and applied to financial fraud detection problems in recent years. These traditional financial fraud detection problems (such as classification-based methods) often suffer performance degradation caused by imbalanced data. Even worse, the frauds often destroy the low dimensional structure of the sub-space on which the intrinsic samples are distributed, and the traditional financial fraud detection methods fail to study the low rankness prior in the intrinsic samples when outliers emerge. We give an effective unsupervised financial fraud detection algorithm based on a low-rank recovery method to solve these issues. Our work has several folds: (1) We adopt the Outlier Pursuit (OP) algorithm and its non-convex variants, which can study the low rankness within the intrinsic samples and detect the outliers without supervision. (2) We solve OP and its non-convex variants by combining Alternating Direction Method Of Multipliers (ADMM) with Generalized Accelerating Iterative Algorithm (GAI). (3) We evaluate the effectiveness of the OP-based methods and compare methods on three data sets, including the German Credit Data, Insurance Company (TIC) Benchmark, and IEEE-CIS fraud Detection data set. The related code is available at https://github.com/jzheng20/FinancialFraudDetectionViaOP.git.