For many years, the conversation around data always involved some sort of controversy. Data is important to every business, and sharing data was something extraordinary to do back in the day – for understandable reasons. Data is worth gold, and in the wrong hands, destructive. Initially, fraud prevention strategies, (or let’s rather refer to the approach that was followed to identify high risk claims) were referred to as the “Black Box”approach. This involved identifying one doctor, or one claimant, and focusing only on this one particular business or person – no data intelligence. This approach was replaced with a business rule-approach. Business rules score a claim based on various rules, and the resulting score identifies those claims required for investigation – limited intelligence.
Artificial intelligence solutions and machine learning fraud models are becoming increasingly important to businesses as an advanced tool, to help extract valuable insights from vast amounts of data. However, together with the tremendous benefits there are challenges implementing machine learning risk strategies – especially when considering that many businesses and organizations still do not have a thorough understanding of the science, sophisticated technology, and the interpretation of artificial intelligence generated data.
Machine learning refers to analytic techniques that “learn” patterns in datasets without being guided by a human analyst. AI refers to the broader application of specific kinds of analytics to accomplish tasks, from the initial enquiry or request for insurance cover to, identifying a fraudulent claims transaction.
In effect, machine learning is a way to build analytic models, and AI, as the use of those models.
Machine learning helps data scientists efficiently determine which transactions are most likely to be fraudulent, while significantly reducing false positives. The techniques are extremely effective in fraud prevention and detection, as they allow for the automated discovery of patterns across large volumes of streaming transactions.
If done properly, machine learning can clearly distinguish legitimate and fraudulent behaviors while adapting over time to new, previously unseen fraud tactics. This can become quite complex as there is a need to interpret patterns in the data, and apply data science to continually improve the ability to distinguish normal behavior from abnormal behavior. This requires thousands of computations to be accurately performed in milliseconds.
Without a proper understanding of the domain, as well as fraud-specific data science techniques, you can easily employ machine learning algorithms that learn the wrong thing, resulting in a costly mistake that is difficult to unwind. Just as people can learn bad habits, so too can a poorly architected machine learning model.