Customers appreciate the insurance industry’s move toward online business processes. It’s quick and convenient to research rates and submit policy applications online. Unfortunately, fraudsters love it too. Digital application processes make it easier to commit insurance application fraud in three surprisingly common ways:
- Fraudsters open insurance policies for fictitious beneficiaries.
- Agents skim premiums or open and cancel policies to make quota and bonuses.
- Customers tweak and falsify application information to reduce their premiums.
However, while digital channels open new opportunities for insurance application fraud, they also provide new data points to fight back. Smart insurers will take advantage of data from device fingerprints, IP addresses, geolocation and more to help detect fraud as early as the point of application. Let’s look at some effective defense strategies powered by analytics and machine learning.
White paper on how to fight insurance application fraud
Since the risk can start as early as the point of application, it makes sense to have strong tools in place to monitor the digital application process. Learn about analytics-driven methods to authenticate applicants to reveal customer gaming, agent gaming and potential future claims fraud.
Verify identity at the point of digital application
Insurers have masses of data – internal and external – that can help determine the authenticity of an application and the individual behind it (if there even is a real person behind it). Here are some effective, data-driven approaches to combat insurance application fraud:
- Monitor application data to see if the same information or device is being reused across multiple identities that otherwise look unrelated.
- Assess past experience for existing or canceled policies that share a data element with the new application, such as the same device ID, address or SSN.
- Find “proof of life,” details you would associate with a real personal identity, such as driver’s license, voter registration or property ownership.
- Analyze the network surrounding the application, looking for unusual or suspicious connections (or lack thereof) among applicants, devices, policies and application data.
Prevent future claims fraud at the point of application
What if you could stop claims fraud before it ever had a chance to get started? What if you could use intelligence gained from the claims detection process to better understand new applications and tag the right ones to send directly to investigative teams?
You can. Network analytics connects the dots by spotting links between prior claims alerts and new applications. What did applications that led to fraud look like in the past, and does this application look similar? Connections can be established not just through people or vehicles (addresses, phone numbers, VINs, etc.), but through any number of attributes, such as IP addresses, devices, bank accounts, repair shops and medical providers.
You can enrich the discovery by applying key scenarios learned from prior claims processing. Through machine learning, a form of artificial intelligence, new information gained through analysis can be fed back into models for continuous improvement.
Insurers have masses of data – internal and external – that can help determine the authenticity of an application and the individual behind it (if there even is a real person behind it).
Identify agent gaming
Unscrupulous agents have a variety of options for gaming the system for personal benefit. For example, they can take premiums from customers without filing the policies with carriers or persuade customers to buy unnecessary coverages to earn extra commissions.
A well-rounded fraud solution uses multiple analytic techniques to lead you to agents who might warrant a closer look. Industry-leading solutions detect agent gaming using the following techniques:
- Machine learning examines patterns of agent behavior and matches it with scenarios that past experience shows were associated with gaming.
- Peer grouping clusters agents with similar attributes – career level, specialization, region, etc. – to better compare their activity to that of their peers.
- Anomaly detection finds agents who are performing quite differently from their peers or who show a radical shift in activity that may point to agent gaming.
- Social network analysis connects the dots around an agent, revealing links and overlaps among entities in the application, such as households, VINs and insured properties.
Together, these techniques can spotlight unusual patterns while reducing the chance of false positives and unnecessary inquiries. Ultimately the system learns from each experience and its outcomes – the positives and the false positives – to continuously improve the analytics each time you run the cycle.
Identify customer gaming at the point of application
Insurers that sell primarily over the telephone or internet are subject to a number of well-known and emerging threats. Customers can falsify information about the primary driver or where the vehicle will be garaged, or get refunds on canceled policies that were bought with stolen cards.
Analytics can spot this form of gaming in real time by setting thresholds that define how much an applicant can manipulate the premium before triggering action, such as messaging, callbacks or blocks. Analytics can also detect unusual patterns of activity, such as multiple policy cancellations linked to the same device.
Insurance application fraud analytics in action
Insurance companies that have invested in strong anti-fraud capabilities have seen dramatic results. For example, SAS partnered with a large US carrier to deploy a fraud solution to identify agent gaming and increase the productivity and throughput of field underwriting, territory managers and internal audit teams.
Using analytics to improve detection, the solution found 10 times more bad-performing agents – 40 percent referred to internal audit, compared to only 4 percent under the legacy process. At the same time, analysis and investigation efficiency improved by 13 hours, and data gathering efficiency improved by two hours.
The company can now do five investigations in the time it used to take for one because the solution brings together the data, the analytics delivers more meaningful alerts, and the audit team can focus on core work rather than data housekeeping.
Ultimately, a robust application and claims fraud solution shuts the front door before fraud has a chance to get in and get started.
- Managing fraud risk: 10 trends you need to watchSynthetic identities, credit washing and income misrepresentation – these are just some of the trends to watch if you’re trying to understand how to manage fraud risk. Find out what’s on the top 10 list of trends according to experts like Frank McKenna and Mary Ann Miller.
- Small-time cheats and organized crime: Benefits fraud re-examinedTo combat benefits fraud, better utilize resources and improve ROI, analytics can detect organized crime rings rather than just small-time fraudsters.
- Rethink customer due diligenceTo streamline compliance and protect against financial and regulatory risk, re-examine your customer due diligence processes and technologies regularly. With new analytical tools, you can monitor customer transactions or personal information in real time, and accurately segment customers by the risk they represent.