Six Ways That You Can Detect Fraud By Using Technology For Insurance

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Insurance fraud costs companies billions of money per year across the globe, making it imperative that insurers take a proactive stance against fraud. Insurance companies should establish a technology framework, tap into advanced automation and analytics, and take steps to prevent it.

At the same time, cost pressures and an exodus of people with claims skills have forced insurers to increase their automation of claims handling. As automation increases, sensitivity to fraud by the claims handler decreases. Professional fraudsters are continually on the lookout for insurers without, or with less effective, fraud prevention barriers.
To avoid expensive litigation and other costly measures, insurance companies must move forcefully against fraud. This begins by adopting a proactive stance toward fraud detection. Companies should not wait for fraud to occur and deal with it after the fact; instead, they should take actions and implement processes that identify potential fraud early and provide the ability to move quickly when fraud is detected.
Moving from reactive to proactive fraud detection takes six steps:


A foundational framework should reflect a fraud-detection strategy that addresses such questions as: How can we check all claims for fraud but ensure fast claim processing? How can we identify fraud before a claim is paid? How can we improve fraud investigation efficiency? How can we keep track of changing fraud behaviors? How can we reduce false positive signals? And finally: What is the best approach to automate the fraud-detection process and predict the likelihood of fraud? Implementing a foundational framework enables management to make better decisions about priorities, resource deployment, and investments.

A foundational framework can range from an “out-of-the-box” solution that automates the institutional knowledge of your claims professionals and enables workflow management to full social networking analysis of the parties involved in a claim. From there, insurers can add a multitude of scoring engines, third-party data capture, criminal history lookups, and many other tools. An important aspect of fraud detection is having a culture in your claims staff that emphasizes the importance of recognizing, identifying, and investigating suspicious claims. Empower your staff to be involved, and then the tools you deploy will function much more effectively.


Knowing the relative level of fraud potential for every type of claim allows the best, and quickest, action to be taken to maximize special investigative unit (SIU) efficiency and savings. With limited resources to devote to fraud, it is important to make sure your investigations can be focused on the items that have the greatest potential for cost avoidance and successful identifications. For example, a theft claim involving the suspicious disappearance of expensive jewelry has a higher potential for being fraudulent than a stolen smartphone or laptop. Examples of common false claim schemes include deliberately destroying property and misreporting the cost of auto repairs.


Fraud comes in all shapes and sizes. In general, insurance fraud can be divided into two categories: criminal fraud, which is perpetrated by professionals habitually trying to milk the system; and cultural fraud, which is a genuine claimant being opportunistic or exaggerating a claim.

Data analytics can be applied to detect fraud. By analyzing past fraud, insurers can use predictive modeling to produce what is called a “Suspicion Score,” value for the propensity of fraud. The process works like this: Adjusters simply enter data, and claims are automatically given a Suspicion Score to indicate the likelihood that fraud has occurred. The technology behind this involves utilizing data-mining tools and applying quantitative analysis.

Even with automation and data analytics, the weakest link in fighting fraud can be your employees. The importance of checks and balances cannot be stressed enough.


Success in combating insurance fraud comes from persistence and good timing. Above all, apply your arsenal of tools — including data analytics and predictive modeling — early and often. Claims should be continuously monitored for fraud potential. As an insurance company, you must target the right claims, at the right time, with the right tools. Luckily, predictive modeling and advanced analytics are coming into play as essential tools for fighting insurance fraud. These tools can be automated, preventing the need for hands-on manual analysis.

By continuously reviewing and rescoring claims using Suspicion Scores, insurers can detect patterns that reveal fraud. Some claims score high immediately at the first notice of loss, prompting your SIU (Special Investigative Unit) to get involved immediately. For others, high scores do not show up until after the claim has been collected.

Monitoring Suspicion Scores is more accurate and more effective than traditional fraud-detection methods. But again, the key is to not rely solely on technology to do all of the heavy liftings — human analysts are required to initiate action after the suspected fraud has been flagged, and your people must follow through with appropriate measures. This is where training employees to identify fraud becomes an important piece of the overall fraud-detection puzzle.


In the world of IT, a “layered approach” refers to using a variety of tools and technologies to tackle a challenge. In detecting insurance fraud, this means throwing the kitchen sink at the criminals but doing it in an organized, well-considered fashion.

Fraud is a complex, multifaceted problem, and no single method can detect all fraud. Each fraud-detection method needs to be crafted to address a specific area. Different rules and indicators are needed for different types of policies and claims. Plus, fraudsters hide in multiple databases, so fraud-detection methods must search them all. Because of the complexity of fighting fraud, it is advisable to bring in outside expertise to help formulate a framework and implement the technology, tools, and methods needed to deal effectively with fraud.


Criminals are ever resourceful, so always be ready to quickly adapt to changes in the ways fraud is undertaken, as well as changes in your industry. For example, professional criminals are sophisticated enough to become familiar with the analytical approaches that insurance companies use to detect fraud and to change their tactics when committing fraud. As fighting fraud becomes more proactive, insurers must spot new fraud trends early and take steps to stay ahead of the bad guys.

Your everyday policyholders may also try to be more creative with their insurance claims when the economy is in a down cycle. Keep your claims staff aware of the type of market conditions the policyholders are facing so the staff can be on the lookout for new and inventive fraud attempts that may be unknown to the software in place.


Companies can use a combination of technology, tools, and approaches to combat fraud. Through it all, industry leaders must never forget that their focus should not only be on the technology tools they use in detecting and fighting fraud but also on the human beings in their own offices. Always emphasize fraud training and awareness, implement checks and balances, and be ready to adapt quickly to changing market conditions.

Inmediate is an insurtech startup from Singapore that is using the latest technology such as Artificial intelligence, Distributed Ledger, and NLP, making insurance processing and underwriting fast, cheap, and flexible. That gives for better processes, lower costs, improved time to market, and new revenue opportunities.

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Introducing Inmediate: a platform on which customers, distributors and insurers using smart contracts connect.

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