ATOVis – A visualisation tool for the detection of financial fraud. (October 2022)

• Record Type: Journal Article
• Authors: Maçãs, Catarina; Polisciuc, Evgheni; Machado, Penousal
• Is Part Of: Information visualization. Volume 21:Number 4(2022)
• Page Start: 371
• Page End: 392
• Publication Date: 2022-10
• Subjects: Information Visualisation -- finance -- fraud patterns -- visual data exploration; Information visualization -- Periodicals; 006.605
• DOI: 10.1177/14738716221098074
• Languages: English
• ISSNs: 1473-8716

Abstract

Fraud detection is related to the suppression of possible financial losses for institutions and their clients. It is a task of high responsibility and, therefore, an important phase of the decision-making chain. Nowadays, experts in charge base their analysis on tabular data, usually presented in spreadsheets and seldom supplemented with simple visualisations. However, this type of inspection is laborious, time-consuming, and may be of little use for the analysis and overview of complex transactional data. To aid in the inspection of fraudulent activities, we develop ATOVis – a visualisation tool that enables a fast analysis and detection of suspicious behaviours. We aim to ease and accelerate fraud detection by providing an overview of specific patterns within the data, and enabling details on demand. ATOVis focuses on applying visualisation techniques to the Finance domain, specifically e-commerce, contributing to the state-of-the-art as the first visualisation tool primarily specialised in Account Takeover (ATO) patterns. In particular, the present paper incorporates: a task abstraction for detecting a specific financial fraud pattern – ATO; two models for the visualisation of ATO; and a multiscale timeline to enable an overview of the data. We also validate our tool through user testing, with experts in fraud detection and experts from other fields of data science. Based on the feedback provided by the analysts, we could conclude that ATOVis is an efficient and effective tool in detecting specific patterns of fraud which can improve the analysts' work.