Online fraud detection needs #AI to stay at parity with the quickly escalating sophistication of today\u2019s fraud attempts.<\/p>\n
Bottom Line:<\/strong>\u00a0Combining supervised and unsupervised machine learning as part of a broader Artificial Intelligence (AI) fraud detection strategy enables digital businesses to quickly and accurately detect automated and increasingly complex fraud attempts.<\/p>\n Recent research from the\u00a0Association of Certified Fraud Examiners (ACFE)<\/a>,\u00a0KPMG<\/a>,\u00a0PwC<\/a>, and others reflects how organized crime and state-sponsored fraudsters are increasing the sophistication, scale, and speed of their fraud attacks. One of the most common types of emerging attacks is based on using machine learning and other automation techniques to commit fraud that legacy approaches to fraud prevention can\u2019t catch. The most common legacy approaches to fighting online fraud include relying on rules and predictive models that are no longer effective at confronting more advanced, nuanced levels of current fraud attempts. Online fraud detection needs AI to stay at parity with the quickly escalating complexity and sophistication of today\u2019s fraud attempts.<\/p>\n Why AI is Ideal for Online Fraud Detection<\/strong><\/p>\n It\u2019s been my experience that digitally-based businesses that have the best track record of thwarting online fraud rely on AI and machine learning to do the following:<\/p>\n