Green Shield Canada
Founded in 1957, GSC is headquartered in Windsor, Ontario with regional offices in Vancouver, Calgary, London, Toronto, Montreal, Quebec City, and Moncton. The company’s stated mission is to create innovative solutions that improve access to better health care for all Canadians.
Insurance fraud takes various forms. One of the most common instances is filing a claim with the intent to defraud an insurance carrier. Health insurance organizations have always been faced with fraud. While it is not possible (at least not yet) to know exactly how widespread it is, the Canadian Life and Health Insurance Association (CLHIA) estimates that 2-10% of all health care dollars are lost due to fraud in North America, with benefits misuse accounting for some of this.
GSC is seen as a leader by clients and advisors in fraud management through the use of sophisticated adjudication systems, as well as its National Provider Registry. Over the last decade, fraudsters have become more sophisticated and the schemes more organized with collusion between members and providers. GSC wanted to enhance its fraud management capabilities to assist in the detection of these networks while improving its detection and prevention capabilities.
Green Shield Canada
GSC decided to deploy a data-driven and learning-based method of fraud detection to drive an enhanced effort to detect and investigate benefits fraud. The company engaged Daisy, which offers technology powered by reinforcement learning based AI (artificial intelligence), to autonomously analyze insurance claims data and identify suspicious claims transactions, as well as individuals and networks that may be committing fraud.
AI enables GSC’s Benefits Management & Investigation Services (BMIS) team to analyze data on a massive scale; evaluating every claim, individual plan member, and service provider. Daisy’s technology generates fraud alerts for a single claim, and when enough evidence of suspicious behaviour accumulates over time for plan members and providers.
The fraud detection team – now armed with evidence based on past history, associations with others and observed patterns – then decides whether to investigate and proceed with building a case. A key feature of Daisy’s fraud detection system that GSC found compelling was the ability to look across groups of individuals to discover networks of individuals, connected in ways that are not obvious (i.e. people who should not be related but share common personal, banking, phone, address, and other information) but whose cumulative behaviour is suspicious.
The team has seen improvements in their ability to quickly access claims information at a provider and member level. This has allowed them to spend less time analyzing reports and deploy more investigative efforts to their files. One-third of the files opened in 2018 were as a result of information obtained from Daisy’s fraud detection system, accounting for 25% of GSC’s total prevention and recoveries.
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