Detecting Telecommunication Fraud with Visual Analytics: A Review

Abstract

The detection of anomalous events in large multivariate data is sought in many domains. Analysis of data is an important fraud detection procedure in detecting suspicious events and prevent attempts to defraud. While now the data is becoming more complicated and difficult as data scales and complexities increase than ever before, the rich insights within the data may be difficult to identify by traditional means and often remain hidden. People require powerful tools to extract valid conclusions from the data while maintaining trustworthy and interpretable results. Hence, various fraud detection approaches have started to exploit Visual Analytics (VA) techniques to reveal the hidden knowledge in such fraudulent activities. Interactive data visualization tools have substantial potential for making the detection of fraudulent transactions more efficient and effective by allowing the investigator to change the representation of data from text and numeric into graphics and filter out subsets of transactions for further fraud investigation. However, little research to date has directly examined the efficacy of data visualization techniques for fraud detection especially telecommunication fraud. In this paper, we present an overview of several fraud detection solutions that use data visualization techniques to detect fraudulent transactions in the telecommunication domain. The paper concludes by discussing how academic research might proceed in investigating the efficacy of interactive data visualization tools for fraud detection.

Big data analytics and auditor judgment: an experimental study

“…The visual analytics model often used to detect fraud is heat maps (Singh and Best, 2016; Leite et al , 2018; Amin et al , 2020). Moreover, Aldhizer (2017) explains that this model provides a graphical representation of different data elements, such as the perceived level of fraud risk in the client’s business processes based on the probability of occurrence and the level of materiality.…”

Financial fraud detection: the use of visualization techniques in credit card fraud and money laundering domains

“…Both frameworks sort the outliers by relevance and then identify a subset of features to optimize the feature selection process. Others have used visualization techniques to discover sequential patterns in CCF transactions (Carcillo et al , 2018; Amin et al , 2020). The idea behind sequential pattern analysis is to discover hidden patterns by clustering the data into groups and then identify the outlying observations that may not be possible to make through traditional statistical queries.…”