Advanced AI for Behavior Detection

July 15, 2020 / 5 min read

Last updated on October 27, 2023.

While faster claims processing is a desired goal for most insurance companies, speed comes with the potential risk of paying more fraudulent claims. Most carriers are cognizant of this problem, which is why they’re looking to leverage artificial intelligence to detect fraudulent activity in insurance claims. But to incorporate those AI technologies into claims fraud analytics, it’s important to understand the four critical components of operationalizing them: variety, value, volume, and velocity.

Variety: Does your AI system have enough data?

Developing algorithms and predictive models for AI claims fraud detection requires data—a lot of data. The more data that’s analyzed, the more effective the system will be at automatically recognizing suspicious patterns and characteristics indicative of fraud. While an insurer’s historical claims data is a good foundation, incorporating third-party data sources provides a more holistic picture because it taps into broader industry data for analysis.

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But integrating third-party data into claims fraud analytics has its challenges. Mapping data from a third-party source to an internal model can take a significant amount of time (a year or more in some cases) and resources. But having an established infrastructure built into anti-fraud AI systems can create more seamless data integration, accelerate implementation, and save insurers money in the short-run.

Another type of variety is unstructured data, such as images, audio, video, text, and social media. Analyzing this kind of data can enhance the scope of fraud detection. For example, image analysis, such as can determine if loss photos submitted by claimants have been tampered with or if the image time stamp is before the date of loss. In addition, text mining technology can read and analyze claim notes and identify trends and patterns associated with certain keywords.

Analyzing a wide variety of data along with internal data sets is essential to recognizing new and emerging patterns and identifying fraud risks.

Value: Are you getting the right insights from your claims fraud AI?

Across industries, businesses struggle to derive actionable insights from data. Nearly 60 percent of organizations suffer from inaccurate business intelligence because of slow or poor access to the right data.^[1] For insurers, part of the issue can be attributed to disparate data within their organizations. McKinsey reported that insurer data is typically “incomplete or miscoded and substantial effort is required to bring the data into working condition.”^[2]

Organizing data often requires investing in data science resources, and insurers are starting to answer the call. According to an industry study, 35% of large property/casualty insurers and 24% of midsize insurance companies are expanding their data science efforts. ^[3] But as data science teams apply analytics, insurers need business analysts to apply the proper business logic to models to get actionable insights for fraud detection.

For example, predictive models that not only score claims for fraud potential but also provide reason codes that detail suspicious behavior and attributes within a claim can help direct SIU on how to intervene and investigate.

Volume: Is your data deep enough to find entrenched fraud in your claims?

Deriving actionable insights from AI fraud detection systems not only requires the right data but also the necessary depth of data. While many people think of data volume only in terms of breadth, it’s equally important that data is deep and rich enough to create effective analytic models.

Fortunately for insurers, claims often include depth of information. For example, an auto bodily injury claim can include extensive details: involved parties, injury details, medical provider information, drugs prescribed, auto body shop, vehicle damage photos, police report, weather report, and more. Analyzing that depth of information—including historical claims data from involved parties and providers—offers a more complete picture of a claim.

While this extent of detail usually isn’t available at first notice of loss (FNOL), it can unfold throughout the life of a claim. And as adjusters gather reports, interviews, and specifics on the event and entities, adding that information is vital for accurate analysis.

Velocity: Do you have the processing speed that AI demands?

A claims fraud analytics system that uses AI is only as good as its processing power. Each claim can have hundreds of data points that need to be analyzed and weighed against each other to determine its risk score. And it needs to be done fast!

Crunching a large variety of structured and unstructured data requires significant speed and capacity that most internal servers can’t handle. A carrier needs the proper tech stack to enable the modeling and analyzing processes. Cloud computing makes it possible. Novarica reports that more than 70 percent of insurers are using cloud computing in some fashion, while 10 percent run most of their infrastructure on the cloud.^[4]

Fast processing also enables a critical aspect of fraud analytics real-time insights. As new information becomes available, it needs to be analyzed to update the claim as quickly as possible to equip claims handlers with the right insights either to process a claim or triage it for further investigation.

Keeping pace in the fight against fraud

Technologies such as AI, machine learning, and predictive analytics are becoming more common in the insurance industry, and they’re transforming all aspects of the business. There’s potential for a strong return on investment in fraud analytics because these technologies can directly impact fraud leakage in the claims area. Whether you’re building in-house analytics or partnering with a vendor, it all starts with the four key components necessary for effective insurance claims fraud detection.

Learn more about Anti-Fraud Claim Solutions.

[1] Digital Insurance. “Poor data access putting many analytics projects at risk”
https://www.dig-in.com/news/poor-data-access-putting-many-analytics-projects-at-risk

[2] McKinsey & Company. “Transforming into an analytics-driven insurance carrier”
https://www.mckinsey.com/industries/financial-services/our-insights/transforming-into-an-analytics-driven-insurance-carrier

[3] Insurance Innovation Reporter. “Insurers Are Prioritizing Data Science More in 2019.”
https://iireporter.com/insurers-are-prioritizing-data-science-more-in-2019/

[4] Novarica. “70% of Insurers Now Using Cloud Computing, With Additional Growth Planned, Says Novarica”
https://novarica.com/70-insurers-now-using-cloud-computing-additional-growth-planned-says-novarica/

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