Machine learning is going to revolutionize the insurance industry; we are already seeing the topic in almost every insurance publication and conference that is in circulation. But, what is it? And what are the benefits and risks? This blog will serve to provide an overview of machine learning and its application in insurance fraud.
• Machine learning is not new technology; yes, it is cutting edge, but it has been around for years and has been applied in many other areas. It has only recently come to the forefront in the insurance space because of the incredible influx of data entering into our industry.
• At its core, ML is the science of designing and applying algorithms in order to learn from past cases. ML uses complex algorithms and scans large data sets to analyze patterns that have not been previously known. When ML platforms are coupled with other technologies, such as decision logic, the results are extremely impressive.
• ML platforms can sort and analyze large data sets.
• ML teaches machines to recognize patterns.
• ML is continually trained by new data being added to the system, as a result, it gets smarter as a carrier uses the system and adds more data into it.
• ML is extremely fast, when compared to manual reviews, it can evaluate a significant number of transactions in real time.
• ML platforms can process an extremely large amount of data to identify unexpected outcomes, previously unseen combinations, or to find analogies to previously detected fraud.
RISKS:
• Machine learning is only as good as the human data scientists behind it AND even more the data and quality of data behind it. Even the most advanced technology cannot replace the expertise it takes to effectively filter the data and evaluate the meaning of the risk.
• It takes a significant amount of data for machine learning models to become accurate, thus companies need to be aware of this potential limitation.
• ML is probably the number one buzz word right now in insurance counter fraud. Accordingly, with any “hot” commercial topic, solution providers are surfacing daily with ML offerings. Be forewarned to carefully research the capabilities of these providers to insure they have true expertise in this area and are not simply jumping on this commercial bandwagon.
There is no doubt that the insurance space is becoming data-centric, that is, data is slowly moving to the nucleus of almost all areas of the industry; from on-boarding, to claims handling, to settlement, data is becoming the new platform to assist with business strategy. With all of this data in circulation, carriers must be able to manage this in an intelligent and efficient way, perfect for a machine learning model. As outlined above, embrace the technology, but be cautious with its application and ensure that the right people are monitoring and maintaining its use.
Stay safe!
Dr. Fraud