3 ways data can help detect and prevent insurance fraud

according to Insurance Fraud Prevention Coalition and the latest report Estimating the economic impact of insurance fraud in the United States, the estimated annual costs associated with insurance fraud It has grown from $80 billion in 1995 to $308 billion in 2022. This is a staggering number and consumers will absorb it in the form of higher premiums. $3,700 per year For the average US family.

While the insurance landscape looks very different than it did in years past as a result of the massive digital transformation that has begun, one thing remains constant. it’s a scam. The promise of using new technology and automated tools to improve both the consumer’s user experience and the insurer’s bottom line is real. That’s why a lot of investments have been made in the last few years. However, the implementation challenges are just as real. Striking the right balance that improves both efficiency and his ROI is not easy. How can you maximize the efficiency of your application, quoting, and claims processes and avoid introducing new or additional fraud? The answer is data. The right data is applied in the right process, not just lots of data.

1. Use third-party data to identify discrepancies

Insurers look to meet the demand for digital-first (and sometimes digital-only) interactions to improve the consumer experience, thus improving underwriting efficiency, providing instant insurance quotes, and processing claims. There are challenges associated with direct handling of processing and maintaining reasonable checks. Balance to identify potential fraud. Insurtech companies provide tools to do this, but their effectiveness depends on the confluence of various data sources to isolate red flags. First party data (data provided by potential policyholders) is the holy grail when provided by good parties. But scammers are not good actors. So how do we determine who is sharing correct and incorrect information?

One way to do this is to integrate Third party data – Especially if the data was collected from a reliable source such as the Secretary of State’s business registry, voter registration, or other reliable public records. The idea here is to put another “eye” on the data as a fact check. The applicant states that the business is at one address for him, but third-party data indicates another. The applicant says he has four employees in their business, but third-party data suggests she has 10. Applicant claims there are no children of driving age in the household, but third-party data suggests that the household has a child as young as her 17. you get the idea. You can match third-party data with first-party data to identify these discrepancies. Sophisticated models also automate the interpretation of this data to help determine what to fly and what to investigate.

2. Standardize data and leverage up-to-date data to improve identity resolution

according to State of Technology Research in Insurance Fraud 2021 Published by the Coalition Against Insurance Fraud, poor data quality and data integration is one of the biggest challenges for insurers in applying new fraud detection processes. In fact, bad data, regardless of industry, us cost company About $3 trillion a year. At the same time, insurers are now using more data than ever before (internal system data, unstructured data, social media data, third-party aggregated data, etc.) to try to detect fraud more effectively. is. This approach has many potential benefits, but without the ability to effectively integrate these disparate data sources, it can cause more problems than it solves. To avoid this, we need to be able to normalize or standardize all these different data feeds. Your goal is to take all these different data points related to a particular business or individual and consolidate them into a Super Profile. However, if we do not apply a common name and address standardization process to each input, what should be a single insightful entity turns into what appears to be multiple, insightless entities. There is a possibility.

Of course, normalizing different data inputs is fine if the data itself is inaccurate. It is important that insurers and insurtechs engage diligently in the data review process when evaluating potential data sources. This is where the adage “measure twice, cut once” comes to mind. Seeing things like fill rates is useful when evaluating data sources, but avoid true qualitative checks if you want to flag legitimate fraud scenarios or create a series of false positives. You can not. In addition to testing the data, make sure the provider explains how the data is obtained and, more importantly, how it is maintained. Businesses and consumers can open, close, grow, shrink, move, experience major life events, change business models, and more. keep up with these Change is very important if you want to leverage that data as one of your fraud detection methods.

3. Leverage predictive modeling

Arguably the quickest and most effective way to prevent fraud from happening is to predictive modelIn the 2021 State of Insurance Fraud Technology Study, 80% of those surveyed said they are incorporating predictive modeling into their fraud detection strategy. In fact, it has been one of the most adopted processes in recent years, up from 55% usage in 2018.

Predictive modeling uses analytics and machine learning to take large amounts of data and build digital models that assess whether new applications and claims may be fraudulent. Not only do these solutions scale over time according to the amount of data and become more accurate, they work across all types of insurance.

The reality is that fraudulent insurance activity cannot be eradicated. This is like whack-a-mole. However, if we can hit more moles than we miss, we can ensure that we are making progress towards our operational goals of increasing efficiency, reducing losses, and reducing costs for both the company and our policyholders. But to get there, data and its good data must be the foundation.

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