Artificial intelligence and machine learning play an extensive role in catching frauds.
Machine learning refers to analytical techniques that “learn” patterns in data sets without being guided by a human analyst. Artificial intelligence refers to the wider application of certain types of analytics to perform tasks, from driving a car to identifying a fraudulent transaction. For our purposes, think of machine learning as a way to build analytical models and AI as using those models. Machine learning helps data scientists efficiently determine which transactions are most likely to be fraudulent while reducing false positives. The techniques are extremely effective in fraud prevention and detection as they enable the automated detection of patterns in large volumes of streaming transactions.
If Done right, machine learning can clearly differentiate between legitimate and fraudulent behaviors and adapt over time to new, previously unprecedented fraud tactics. This can get quite complex as you need to interpret patterns in your data and apply data science to continually improve capacity to distinguish normal behavior from abnormal behavior. This requires thousands of calculations to be performed with millisecond accuracy.
Preparation for the Worst-Case Scenario
Because the schemes of organized crime are so mature and adaptable, defense strategies based on a single , one-size-fits-all analytic technique will produce sub-par results. Each use case should be supported by expertly crafted anomaly analytical technique that is suitable for all lead to unsatisfactory results with the problem at hand. Hence, both supervised and unsupervised models play an important role in fraud detection and need to be integrated into comprehensive next-generation fraud strategies.
Familiarity with Behavioral Profiles
Given the complexity and speed of organized fraud circles, behavior profiles need to be updated with every transaction. This is a key component in helping financial institutions anticipate individual behavior and implement large-scale fraud detection strategies that differentiate between legitimate and illegal changes in behavior.
Generic Behavior Models are not Enough
In order to maintain a positive consumer experience, a dedicated fraud analysis should be used to assess the “tricky” questions. This is where advanced profiling, fraud-specific predictive features, and adaptive skills break away from generic behavior analysis. In a world of real-time payment processing and rapidly changing consumer preferences, generic behavior models are insufficient for cross-channel fraud solutions. Because when and how someone decides to make a transaction is not as predictable as the likelihood of canceling a fitness club membership.
