The amount of scams in iGaming since the coronavirus pandemic began has not been significant
A recently released analysis from a global credit bureau last week focused on reviewing current global online fraud trends. TransUnion found that telecommunications, e-commerce and financial services industries have been increasingly impacted by fraud, while online gaming saw no major problems in that same area. The company analyzed two periods from January 1 to March 10 and March 11 to April 28, to reflect the changing economic environment due to the COVID-19 pandemic. Despite the increase in the overall fraudulent transaction reports, the online gaming industry saw a reduction mainly thanks to artificial intelligence (AI) features that have been added in recent years to online its operations.
These two periods were selected based on the fact that the World Health Organization (WHO) officially recognized the COVID-19 pandemic on March 11. Overall, the number of suspected fraudulent transactions increased 5% during the post-pandemic period, which means that over 100 million suspected fraudulent transactions were reported. In contrast, online gambling saw nearly no changes in the number of suspected fraudulent transactions – just 1%. But, even though online gambling increased a whopping 64% since the pandemic started, the number of suspected fraud reports has decreased by 43%.
According to Angie White, Senior Manager at TransUnion’s Global Fraud & Identity Solutions Group, there are several reasons for this like fraudsters having gone elsewhere since the financial information is not shared with these companies. On top of that, gaming operators have been adding device-based reputation checks, which are powered by AI. These machines allow operators to see links between devices and accounts, thereby stopping fraud rings more efficiently. Another key aspect that AI machines facilitate is evidence sharing, which allows operators to prevent a device from playing if it has a previous history of abuse or fraud.
White added that operators implementing AI devices should “assess the quality of the data that is being used to train the system, how effective are they in predictive analysis, and are the results explainable and actionable.” So, operators can refine their fraud catch and reduce manual reviews.