Insurance fraud is a major concern for insurers worldwide, and it is increasing year after year. The most common buzz in the Insurance industry is a fraud claim, with the auto and workers’ compensation business segments being the major contributors. Frauds are typically committed by an individual or a group of fraudsters to inflate claims and eventually profit from a loss. And the rise of analytics opens up a world of nearly limitless opportunity for industries such as insurance, where companies have long relied on information.
Because of cost concerns, the industry has been slow to adopt new Big Data analytics, and regulation may be the future limiting pressure. Previously, fraud detection was left to claims agents, who had to rely on few facts and intuition. New data analysis has introduced fraud review and detection tools in other areas such as underwriting, policy renewals, and periodic checks that integrate seamlessly with modeling.
The role of this data in today’s market varies depending on the insurer, as each weighs the cost of improving information systems against the losses caused by current fraud. This frequently boils down to whether fraud is causing a bad enough customer experience that infrastructure investments will improve fraud detection and simple customer claims processes.
Personal information must be protected, but recognizing fraud patterns necessitates a large amount of data from underwriting, claims, law enforcement, and in some cases, other insurers. When integrating these sources, each new piece of legislation has only raised the protection bar. Once this data has been collected and properly utilized, insurers must determine its accuracy. For fraud predictions, modeling frequently relies on past behaviours, but criminal practices change quickly enough that some of this analysis is useless. Data quality evaluation has become difficult.
While analysis has proven to be a difficult task to master, insurers today are reaping numerous benefits. Fraud detection has comparatively improved, and systems are now robust enough to provide real-time analytics. With the increase in knowledge about various tech tools through global finance & insurance conferences, such as the Money 2.0 Conference, insurers can now scan for fraud before approving a policy or claim, pushing Big Data from a siloed fraud unit to field agents.
Creating A Space In The Market
Data and systems for storing and processing fraud detection, traditionally managed by fraud units and internal auditors, can now often be maintained by IT. With automated processes becoming more popular, it won’t be long before they’re playing a more significant role in the work of prevention inside that department. The availability of real-time services will increase the importance of information technology in budgets and decision-making. Team members are expected to be well-trained to understand the modern threat, regardless of whether they have an IT or fraud background. As many units expand, team members work double shifts as IT experts and fraud analysts. They’re entrusted with knowledge about both facets of crime for operational versatility and make up for a lack of expertise in one area by doubling up their skills in another place that complements it without duplication.
Fraud Claims: Present Scenario
Fraudsters are opportunistic creatures always looking for ways to exploit the weakest links in the chain. If you don’t cover every avenue, they’ll worm their way through places that are still susceptible- and in some cases, nobody else knows about them either. One troubling example of this type of fraud includes ghost brokers who only break open new policies on products or services offered under specific insurance plans- usually products where individual risk is high for one reason or another.
Analytics technology can equip you with accurate time information about potential fraud at every stage, from pre-filing coverage all the way through to payment processing and investigating claims post denial. This could be crucial when identifying those shady brokers who work exclusively within ghost brokering – which happens when an insurer promises creditworthiness to get policies sold but then never pays out a claim after notifying policyholders by mail weeks later that they won’t honor claims without further verification (such as verifying employment history).
Existing Operations & Challenges
Big Data analysis is primarily driven by IT imperatives rather than core business operations. Analytics are frequently introduced on a project basis, and if a benefit is demonstrated, analytics platforms are expanded to include more divisions.
Insurers may first use these techniques in marketing or other customer service areas, but fraud detection units benefit from the tools and analysis. The main reason for introducing analytics into business is to determine its present value and build a case for a consistent return. It becomes a matter of people versus power.
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From Silos To Ever-Present Fraud Systems
Previously, systems could not communicate with one another and were frequently siloed due to a lack of integration technology. As insurers transition to new services, each will be slightly different, so some will have legacy issues. In contrast, many others will have robust systems that can pull data from multiple sources.
Unfortunately, even insurers that have made significant investments continue to operate in silos due to concerns about inappropriate information sharing within departments. Many customers’ information can only be accessed by the department in charge of their policy. This means that an auto policy division will not have access to much information from a homeowner’s insurance division.
Data Security and Transparency
The fraud team can safely and securely enhance, modify, and update the data by storing the data in a separate location. This also assists a fraud team in keeping data on internal systems and away from Web-based threats. When data is lost or stolen, insurers suffer a significant loss of credibility.
As the data is being saved and analyzed through fraud detection, it falls to individuals at different stages of a policy life cycle – from underwriters to claims adjusters – to ensure that policyholders are comfortable with the potential of their information being monitored for foul play. This emphasis on transparency has helped ensure no hidden caveats related to how data is collected or what happens when there are any fraud-related findings.
What Makes Today’s Fraud Detection Unique?
The location of fraud detection in relation to the insured has shifted. Insurers can now run predictive and entity analytics at multiple touchpoints, essentially as new information is added. This improves fraud detection capabilities and allows an insurer to assess fraud risk. Some have begun to provide high-priced policies to risky policyholders to drive them to other service providers.
Today’s insurer has shifted from reactive to proactive to keeping bad business off its books. Insurers see a financial benefit from making significant efforts to keep fraudulent activity out of the business cycle by detecting it during signup.
The push for Big Data and analytics for fraud is accompanied by a clarion call for automation and modeling. Unfortunately, a pure automation operation can generate as much opportunity for fraud as the market already has by producing exploitable data pattern recognition. Fraud detection still requires human intervention, and even the most advanced systems deliver a data product rather than a finished piece of information.
Data is essential in the way insurance companies operate today. However, they are still backed by humans who rely on intuition and instinct; they will always need us. A perfect marriage between machines and humans allows insurance investigators to detect fraudulent claims at a higher rate. With an analytics backbone providing high levels of objectivity we will find ourselves at the top.
Finally, insurers must decide whether to absorb the cost of implementing new fraud detection capabilities today or maintain current operations hoping that analytics will standardize and become less expensive before increased competition presses margins too thin.