Chapter 3: Business-IT alignment in fraud analytics: fostering collaboration between domain experts and data scientists
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Fraud analytics refers to the use of Big Data Analytics to detect fraud. In government, two groups of stakeholders are usually involved in this process: domain experts knowledgeable in fraud detection and data scientists knowledgeable in analytics. However, data scientists in government are rarely knowledgeable in the business domain (fraud detection) and domain experts do not always have an information technology (IT) profile. Thus, ensuring collaboration between the business and IT sides of fraud analytics is a key challenge for governments. Alignment is increasingly important to detect fraud efficiently, because the complexity of fraud, as well as the techniques used to detect them, keeps increasing and makes collaboration necessary. The goal of this chapter is to formalise the fraud analytics process and to illustrate this key alignment challenge. For this, we examine two case studies from the Belgian Federal government: the detection of tax fraud and of social security infringements. Data from these two cases has been collected from 21 interviews. As a result, we infer two fraud analytics processes and identify three crucial moments where alignment between business and IT is needed: the identification of requirements of the business team before performing the analytics, the presentation of the output of the analytics to the business team, and feedback from the business to the data scientists. In order to foster this alignment in fraud analytics, we suggest a methodology drawing from agile methods, participation methods and design thinking literatures.