Increased Fraud Recoveries and Avoidance

Large North American insurer increases fraud recoveries and investigative capabilities

Client profile

Large North American Insurer

Fraud Detection


North America

Our client was the Canadian headquarters of one of North America’s largest insurance companies. This division was responsible for group health benefits claims for both individuals and group policies. Annual claims processed are in the billions of dollars.

The challenge

The client was seeking to augment its fraud investigative capabilities with an AI system. Current fraud recoveries were less than the industry stated belief that 3% to 10% of all claims had some element of fraud or abuse. The first step to building an operational AI-based fraud detection capability was to assess the amount of fraud, waste and abuse that was detectable and recoverable or avoidable. Once the magnitude of the opportunity was understood, a business case could be developed to enhance existing capabilities. The client identified that Daisy’s AI platform for insurance could be a fit to achieve the project objectives and engaged Daisy in a trial.

Our solution

Daisy implemented its Fraud Detection solution for the dental line of business during this trial to meet our client’s objectives and to build a business case for enhancing fraud detection and avoidance capabilities.

  • Daisy Fraud Detection: Automated AI-based fraud detection system that would identify potential fraud for both historical claims as well as in-process claims before adjudication was completed.
A typical Daisy implementation requires two months to onboard the client’s data and to work with the business operators to understand the client’s existing process and capabilities in claims processing. The client already had a set of fraud flags that were used for fraud detection and had historical labels for previous fraudulent claims and individuals. Over a six month period, Daisy delivered its system which included integrating the client’s flags with Daisy’s existing probabilistic rules. Predictive models using the client’s labels were autonomously deployed, fuzzy logic peer analysis of patients and providers was autonomously deployed, and social network analysis of all entities was autonomously deployed.

The client dedicated a single investigator to work with the Daisy system. The client was willing to retroactively seek repayment from dental providers through legal means.


The Daisy system applied to historical data discovered cases of new fraud that had not been previously investigated that one investigator was able to follow up on. Immediate recovery opportunity from these cases resulted in significant post-payment recovery and future fraud avoidance. Increasing the bandwidth of investigative effort resulted in significant annual fraud avoidance for one line of business.

183 Cases of New Fraud Found

By working with Daisy, the client discovered 183 cases of new fraud found that had not been previously investigated.

$100K in Immediate Recovery

$1M in Future Fraud Avoidance

$10M in Fraud Avoidance


Female and male insurance colleagues collaborating on a tablet


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