Part 3: Fighting Fraud
Fraud is as old as humanity itself and can take an unlimited variety of forms. According to PwC’s 2020 Global Economic Crime and Fraud Survey, 47% of global organizations have been the victim of a fraud (and many of the remaining 53% may have been without realizing it, so the number is almost certainly higher) for a total estimated loss of $42B [PwC, 2020]. The European Central Bank (ECB) reports that the total value of fraudulent card transactions annually amounts to €1.8 billion ($2 billion) [ECB, 2018].
Fighting fraud, and more generally detecting anomalies in data, is a vital task that has an enormous impact in multiple areas, such as finance, security, healthcare, and law enforcement. It recently has gained a lot of interest among machine learning practitioners, and whereas until now companies in most domains used a mix of human-based and fixed rules–based analysis fraud detection is now becoming more of an automated process, with machine learning playing a key role.