Every major insurance company now understands the importance of implementing automated fraud detection and risk analysis into their core processes. Without it they’ll face ever-evolving fraud schemes and illegitimate customers, falling behind in an increasingly competitive marketplace.
Implementing a fraud detection platform can be extremely easy and inexpensive, especially if the provider has a ready-to-go implementation with your core insurance system like Guidewire or Duck Creek. But many insurers worry the process will become overly complicated or drawn out if they lack historical fraud investigation data.
This should not be a reason to wait! With over 150 global implementations, FRISS has made it easy to build fraud models and get an insurer prepared to tackle today’s biggest threat to their combined ratios.
What About Red Flags?
To start, the insurance industry has used red flag lists for as long as I can remember. And while it’s only the first step, it can still get the job done. Automating red flags and providing actionable insights brings you a step closer to ideal fraud identification. After all, it’s better to use something than nothing at all.
Then, remember that Rome wasn’t built in a day. Sometimes it’s important to start slow. Investigating claims based on expert rules will quickly build up the fraud flags needed to automatically create useful fraud models.
The next step is to integrate internal and external data sources. These will begin to supplement your rules and red flags with information that would otherwise be hidden from your investigators. You’d be surprised at how well your current and historical policy and claims data will provide you with otherwise unavailable insights. There’s more there than you realize, and the right technology will immediately provide insights based on this data alone.
By now you will start detecting more opportunistic fraud than ever before. Investigators will be better armed to deal with large-scale fraud, and much of the smaller day-to-day fraud that really adds up will be automatically eliminated. You’ll see significant improvements in loss ratios and your reputation will begin to speak for itself in shunning away those who aim to deceive you.
Better with Age
With this combination you will also see consistency in claims processing, also leading to better models. In a matter of time, you will have enough historical data to create your fraud models.
Before you know it, you’re able to expand your horizons to continue to detect more fraud and reduce your loss ratio. And guess who just made insurance more honest?
About the author
Jim Murphy has a specialized Master’s degree in Economic Crime Management and is a 30-year veteran of the insurance industry. After serving as a police officer in New England and spending years running a Special Investigations Unit analyst team, he’s now the Vice President of Products for FRISS, helping insurers fight insurance fraud and making insurance more honest.