Predictive analytics is applied in many different organizations and types of business. Insurers use it to fight fraud that has reached an all-time high. Predictive analytics in the insurance industry is nothing new, but over the past decade, there has been a huge shift in the way insurance companies operate, with over 80% of insurers using predictive modeling to detect fraud. Digital Insurance brought this topic to us in their article, “Eighty percent of insurers use predictive modeling to fight fraud, study.”
The primary purpose of extending insurance to individuals, companies, assets or properties is to protect them from potential losses. Despite the popular myth that it is victimless, insurance fraud is very much real. In fact, it has become a huge problem.
Insurance enterprises have started realizing the importance and utility of high-tech solutions to deftly confront such digitally-undertaken insurance fraud in the current times and the future. Today’s organized, digital fraud requires advanced, data mining, analytics, and customized fraudster behavioral pattern-based algorithms to be programmed for proactive, timely scam detection.
Making data accessible is key, and that’s something we know a little about. Whatever you are searching for, it is important to have a comprehensive search feature and quality indexing against a standards-based taxonomy.
Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.