Since its foundation in the early 2010’s, this multi-line pan-Asian insurance company has grown rapidly across the region. Establishing itself as an innovative, dynamic player across several key markets, including Hong Kong & Macau, Thailand, Indonesia, the Philippines, Singapore, Vietnam, Japan, and Malaysia.
When this carrier entered the Singapore insurance market, it quickly established a reputation for utilising the latest digital technology to change the way people feel about insurance. By delivering on this vision this carrier is now the largest online insurance company in Singapore.
The Situation: An urgent need to accurately identify suspicious activities in real time
Prior to working with Shift, this carrier relied on a business rules-based solution in its commercial group policies line of business. Its claims handlers manually processed hundreds of thousands of claims annually to identify suspicious activities. The inefficiencies in this process resulted in a considerable amount of fraud, waste and abuse. To improve fraud detection, increase the number of claims that could be straight through-processed (STP), and improve the customer experience, the insurer recognised that it needed a new solution to identify potentially fraudulent activities in real time
The Solution: Identifying fraud, waste and abuse cases in real time with an AI-native engine
Processing over 500,000 claims each year, this carrier recognised that to provide an exceptional customer experience it needed to pay claims faster and more efficiently. At the same time, it needed to improve its ability to identify fraud, waste and abuse cases in real-time. After an extensive search, it selected Shift Fraud, Waste and Abuse Detection.
The insurer was drawn to the solution because it could help them find fraud quickly—prior to claim settlement and payment. Shift Fraud, Waste and Abuse Detection was attractive thanks to its ability to analyse textual information in claims handlers’ notes, identify outliers using anomaly detection, and recognise provider fraud using link analysis technology. In addition, the technology’s SaaS-based delivery model and excellent client references gave the insurer company confidence that the solution could be deployed quickly and successfully.
The project began with Shift Data Scientists extracting and consolidating data from three different legacy systems. This data was then merged with external third-party data, something that was not possible before using the insurer's in-house solution. Shift initially developed fourteen different fraud models to identify fraudulent activity. These included duplicate claims, overcharging, bundling, and excessive mitigation costs. Since Shift Fraud, Waste and Abuse Detection went into production, five additional fraud models have been identified and deployed. “The collaboration between the Shift team and our company was fantastic. Shift really understood our business,” says the insurer.
The Result: Immediate cost savings and increased straight through claims processing
The solution was launched as part of a larger claims workflow automation project. Within the first few months the solution had saved the insurer over £100K. According to the insurer, “We are really happy with the results we have achieved using Shift Fraud, Waste and Abuse Detection.”
Working with Shift has enabled the carrier to do more in-depth investigative analysis thanks to a more granular view of claims data. According to the insurer, “80% of the fraud cases identified as suscpicious by Shift Fraud, Waste and Abuse Detection would have gone undiscovered before.” For example, new cases were identified by using anomaly detection to discover that certain medications were being over-prescribed, and by using link analysis to identify provider network fraud.
In addition, using Shift Fraud, Waste and Abuse Detection has helped with the ability to support same day claims processing, resulting in improved customer experiences. “When handling as many claims as we do, this is an important competitive advantage,” according to the insurer.
