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Over the years, health plans have become increasingly focused on harnessing technology to identify and prevent fraud, waste and abuse. In the not-too-distant past, plans depended on random sampling, rules based and SQL queries, data mining and huge server-based solutions. These approaches were resource intensive and sometimes created provider abrasion.

Special Investigations then turned to features like geospatial analytics, the use of predictive analytics, and anomaly detection which began to reduce false positive rates and streamlined investigative work.

But what about now? As fraud becomes more complex and more costly to health plans, the SIU looks to advanced analytics and data-driven insights to improve program integrity and reduce potential fraud even further. Modern machine learning and AI offer finely tuned detection models, unsupervised learning methods, aggregated and external data sources, social network analytics, and learning models that identify bad actors with more accuracy and efficiency and generate fewer false positives. Over time, as the capability of AI increases, the SIU will realize greater results.

So, where do you fall on the AI maturity curve? Download our infographic to find out!