McKinsey estimates that US health care insurance companies could reduce fraud waste, and abuse (FWA) by $20-30 billion by using machine learning . Historically most of the fraud detection in health care insurance has been handled through manual review and basic automation. This is changing now. More companies have started to look into machine learning to enable more efficient fraud detection.
As any major US health care insurance provider, Aetna processes and pays hundreds of thousands of claims per month. Aetna’s SIU (Special Investigation Unit) focuses on identifying and investigating instances of fraud, waste, and abuse. SIU analyzes claims and patient data to uncover cases of overbilling, incorrect coding, excessive services, and duplicate claims. However, it’s not remotely possible to manually process and investigate every single claim that comes through the billing pipeline, which means that an unknown volume of fraudulent claims is being paid every month, resulting in a value loss for Aetna and its customers.
Building a machine learning platform for fraud detection
Aetna’s Analytics and Behavioral Change Organization works on applying advanced analytics and machine learning to solve different business problems within the organization. Several years ago it partnered with Aetna’s SIU to transform fraud detection and recovery process.
The starting point was to build a data pipeline. Due to the complexity of the data and variety of formats and storage locations, it usually is one of the most time-consuming tasks to bring all the data together in a format that allows applying machine learning techniques to it.
The next step was to build and train machine learning models which generally fell into two categories. The first category aimed at spotting the signs of fraud that wasn’t new to Aetna. Those models were trained with existing data from recovered FWA claims. Among examples of such fraud was an abnormally large number of patients per day or an unusual repeated combination of Current Procedural Terminology (CPT) codes in provider’s claims. The second and most advanced category helped to identify outlier repeating patterns in the data which might signal a new type of fraud.
However, work didn’t stop with building and training the models. Another critical task was to integrate the model’s output in current business operations. Aleksandar Lazarevic, a senior director at Aetna’s analytics organization, who is also in charge of the machine learning fraud detection program, says: “We didn’t build only the data pipeline, we were also able to build a tool that automatically sends all our findings to SIU.”
Going forward Lazarevic’s team plans to focus on proactive detection and prevention of emerging fraud patterns as well as uncovering the inefficient use of provider services and even areas of insufficient provider coverage.
One of the biggest challenges of machine learning initiatives is acquiring and retaining technical talent – data scientists and machine learning developers . However, to fully realize the potential of cutting-edge technology and tightly integrate it in company’s daily operations, Aetna should also focus on hiring and training employees that will bridge the gap between technical and business teams. These employees will combine a thorough understanding of claim payment processes with knowledge of statistics, analytics, and machine learning techniques. By leveraging cross-functional skillset, they will be able to identify high impact cases for machine learning application from a business perspective and work with technical teams to ensure that technology closely follows business needs.
BCG estimates that after the claim has been paid, the probability of recovering the money is approximately 1%. It is also way less costly to hold and analyze incoming claim than investigate and recover the one that has already been paid . Current application of machine learning to fraud detection mostly focuses on identifying FWA in paid claims with further recovery. One of the areas where Aetna could focus machine learning initiatives is identifying and holding suspicious claims before they are processed.
Current machine learning algorithms mostly rely on uncovering fraudulent patterns in historical data and applying the findings to spot fraudulent activities on new data. However, insurance fraud schemes are continually evolving. Fraudsters come up with brand new ways to game the system every day. One of the questions is – how could machine learning be used to effectively spot fraudulent patterns that have never occurred before?
Another important question is how to overcome obstacles to machine learning adoption in a large organization where challenges such as intense competition for technical talent on the market, fragmented data systems, subpar data quality and lack of long-term machine learning implementation strategy might get in the way .
 Healthcare.mckinsey.com. (2018). Using machine learning to unlock value across the healthcare value chain. [online] Available at: https://healthcare.mckinsey.com/sites/default/files/2018_Using-machine-learning_Infographic.pdf [Accessed 8 Nov. 2018].
 Schiller, K. (2018). Aetna is taking on insurance fraud with machine learning. [online] Available at: https://arcweb.co/aetna-fraud-machine-learning/ [Accessed 8 Nov. 2018].
 Marr, B. (2018). The AI Skills Crisis And How To Close The Gap. [online] Available at: https://www.forbes.com/sites/bernardmarr/2018/06/25/the-ai-skills-crisis-and-how-to-close-the-gap/#78d90d8b31f3 [Accessed 10 Nov.2018].
 Selikowitz, D. (2018). The right medicine: three practical steps to reduce the cost of public health fraud. [online] Available at: https://www.centreforpublicimpact.org/three-practical-steps-reduce-cost-public-health-fraud/ [Accessed 10 Nov. 2018].
 Falcon, W. (2018). 4 Reasons Why Companies Struggle To Adopt Deep Learning. [online] Available at: https://www.forbes.com/sites/williamfalcon/2018/07/05/4-reasons-why-companies-struggle-to-adopt-deep-learning/#53e2d65b4cda [Accessed 10 Nov. 2018].