Visualisation Tool to Support Fraud Detection

Automatic fraud detection and prevention are challenging problems that have attracted the attention of many researchers in academia and industry. Over the last few years, many improvements have been achieved, especially in predictive models based on Machine Learning. However, a considerable amount of these models only provide a prediction score and a short explanation which may not be enough to make informed decisions. This paper presents a visualization tool that aims to assist fraud analysts in making informed decisions and increase their effectiveness in the detection of fraud. To this end, we designed three visualisation models that apply state of the art techniques to support the analysis of fraudulent transactions. To demonstrate the analytic capabilities and benefits of the proposed tool, we discussed a real use case scenario and conducted user testing with real fraud analysts. Through the feedback from both studies, we were able to conclude that the tool is an asset to facilitate the detection of suspicious events as well to improve the analysis times of the fraud analysts’ work process.