How can machine learning be used to detect fraud?

While I agree on all the ways mentioned??here, it would have been great if it had more depth. A user buying a small coffee everyday can definitely buy a 1000$ watch on an anniversary. Perhaps the user was conscious of the?caffeine intake?:) Identifying abnormal patterns is the key to the success of eliminating fraud. I think AI/ML systems should learn from historical fraudulent activities for abnormal patterns and not just apply the exact same pattern to detect but predict as well. AI/ML designers should keep in mind, while they improve to predict the patterns based on existing payment systems, by the time they have robust prediction systems, payment technology would have changed big time. Perhaps users no longer use physical payment cards. Instead crypto is in full swing and users are not entering username and password anymore and authentication is happening by validating bio metrics such as fingerprints, voice recognition, face recognition and eye recognition etc., So AI/ML learning should keep up with the pace of the systems they try to protect.?I do appreciate the initial thought process here. Well done :)

Machine learning can be used to determine basket bundling by analyzing previous purchase behaviors and looking for patterns indicative of identifying products that are being purchased together. Understanding the product combinations and the strength of these relationships is valuable information that can be used for?making?recommendations, cross?selling and up selling, and offering coupons and promotions.?

The risks to privacy might outweigh the benefits of fraud detection. Within enterprises, #UEBA (User Entity Behavioral Analytics) has been used for many years to detect security threats. One of its challenges is that the entity associations to an identity are limited and often don't include user enitities external to the enterprise. Most would not willingly provide an employer to personal email/social media accounts, credit/loyalty cards, personal devices, etc. This information could be useful to detect data exfiltration from the enterprise. To whom would private individuals release this type of data? How would the financial transactions be aggregated and associated to the individual? How would this data be secured? Would an indidual be able to opt out. Many questions...

Machine Learning awards us the opportunity to use a wide universe of data to gain insights. We see fraud alerts already using this technology: credit card/payments & travel come to mind quickly as I personally experience these as alerts for identified outliers or changes in expected behavior or pattern. However, this technology can be further developed in country of origin (or other criteria) and procurement, healthcare/pharmacy claims/care, authenticity, and even freight industry with accessorial charges. These industries have enough data to create a base and train the model. They can also apply many more scenarios at once, producing information in real time to make decisions.

Machine Learning can assist with fraud detection in many ways that will increase our security. One of those ways is to sample historical transaction data and identify those correlations of variables that lead to fraudulent activity. Using that baseline, training models can be created that identify transactions, amounts, frequency, and accounts that correlate with fraud.