Games of chance spur excitement and anxiety at card tables in Las Vegas and casinos around the world as the stakes run high.
Exhilaration comes with potentially winning good money. That chance has fueled the gambling industry for generations as patrons play hunches. The specter of cheating also hangs over the tables. There is a wariness of vulnerability to dishonesty, especially by professional cheaters.
Some gambling dynamics include: risk appetite, taking a chance, thrill of guessing other players’ positions, and urge to beat the odds and win.
This is equally true of fraudsters who play the game of cheating insurance companies.
One of the most important card tactics is to closely scrutinize spoken words, body language and opponent movements. These habits — called “tells” — can expose a player’s position.
Cataloguing “tells” can be as important as the cards themselves. Detecting and quickly analyzing them can neutralize deception by other players. Bluffing is a major deception technique at card tables. And that involves blatantly misrepresenting facts through words and movements.
Does all that sound like an insurer’s traditional first notice of loss (FNOL)? Or the landscape of insurance fraud?
Neither claimant nor insurer flip over all their cards at the outset. For insurers, that mainly stems from workflow and dataflow constraints and inefficiencies instead of a desire to deceive.
Claimants are not revealing their hands, perhaps because the insurer can seek only fairly limited information due to those inefficiencies. All the better for fraudsters to disguise their hands — weak or strong.
“Customer acquisition in auto coverage ... is extremely competitive.”
An insurer’s securing of facts at the point of claim also is limited by the need to make the customer experience as painless as possible. Insurers aren’t willing to risk losing customers due to poor service. Customer acquisition in auto coverage, for instance, is extremely competitive. Rapid and friendly service is a must. The traditional FNOL process thus is not an all-out inquisition for potential scamming.
Until now, automated background analytics and third-party data that might catch fraud schemes also are limited as a result.
New data, new workflow
The vast majority of incoming claims are honest reports about unfortunate events. But to speed claims, the initial information-gathering was designed to be limited. So the insurer does not know as much as it could at that critical point. Nor does the claimant know what the insurer knows.
That inherent lack of transparency in the old-school FNOL process leaves room for the fraudster’s “bluffs.” So if the FNOL process starts with just a handful of general questions and no analytics spinning in the background, and that limited information satisfies the “house,” then too often it’s game on for scammers.
Consider the traditional workflow of an auto collision claim. A third-party claimant calls or logs in. The claimant provides basic information: date, time, location, car, other vehicle, drivability, plus a guesstimate of fault and injuries.
Mind you, this blinds insurers from reading many potential “tells” by fraudsters because the interaction is not in person. The insurer then notifies the claimant that an adjuster will be in contact shortly, thus breaking that important initial connection. The contact is limited. And more important, it cannot be recreated at a later date.
The insurer’s own policyholders are treated in much the same fashion, even though insurers have warehoused a lot of information on that customer.
“The contact is limited. And more important, it cannot be recreated later on. “
Those limitations can make the first contact a potential fishing expedition by fraudsters who are probing for insurer weaknesses.
As technology rapidly advances with great strides in mobile imagery, mobile connectivity, and data integration, the new work and dataflows can transform FNOLs. They are changing the game with more speed, information accuracy, volume and transparency.
The newly developed claim flows are connected, automated and pre-scripted, and are very “data-robust.” Resource deployment is better-served with precise protocols on complexity analysis, and getting the right loss to the right claims associate.
Consider: Millennials expect to connect richly to their insurance company from the scene of a vehicle accident. This includes submitting a photo or video of both vehicles, the scene itself, street signs, traffic controls, parties, police report, registration and more.
Millennials are fully attached to their smart phone. These digital natives hold that instant claim connectivity and dataflow in their hands.
Forward-thinking carriers can absorb this information in an instant. They can immediately synthesize it with their in-house and third-party intelligence on the risk insured by a policyholder or third-party claimant reporting a claim against their insured.
And that can happen in real time. Towing and body-shop preferences are pre-filled. Accident-scene telematics data can be transferred instantly through opt-in arrangements. Even procuring replacement parts can begin with quick supply-chain drop shipments.
Internet of Things. The Internet of Things (IoT) is enabling connectivity of more sensors and monitors. This produces more data and documentation on the pre- and post-loss state of insured risks. Liability calls and injury estimations can be completed often from the crash scene.
This information flows in before the engine even cools. Claim history is immediately analyzed, subrogation is triggered, accurate reserves are set, site weather is documented, and accident-scene losses are determined. A host of data-set pings take place behind the information technology curtain to instantly validate honest claims or expose them as suspicious.
Transforming data into intelligence
Transforming data into shared and transparent intelligence applied at the point of claim can will change the game in customer service — and fraud detection. Most of what may have taken weeks to sort will be analyzed and acted upon in minutes. The three major claim department drivers will be well-served in the transformation — retention, severity and loss-adjustment expense.
Customer retention is king, and the new point-of-claim workflows will achieve great gains in fast-track claim payments, electronic fund transfers, and claim-service feedback by customers. Claim severity will be held in check by increased customer satisfaction. Adjusted loss expenses also will improve.
The increased velocity of decision-making supported by automated intelligence also falls in line with gains being made on the policy side. New point-of-claim strategies will align well with point-of-sale customer enhancements for a streamlined enterprise experience. While ideal for auto coverage, this approach can carry into other lines of coverage and loss types.
Sensors and monitors collect images, weather events, delta V impacts, temperatures, humidity, fresh water/salt water presence, intrusions, service maintenance and much more. The potential application of this data to claims is being mapped and married to the ISO ClaimSearch™ match-reporting process. Vast stores of data provide intelligence that allows for more speed and accuracy in making claim-resolution decisions.
The analytic superhighway is being built before our eyes.
Monitored data transformed into intelligence can lead the charge in fraud-fighting.
Telematics. The term “telematics” started as sensed vehicle data as the key ingredient in user-based insurance measurement. Now that term is more-inclusive. It encompasses claim applications, and considers other sensed data such as “smart highways,” monitored homes, building and equipment. They’re all part of the burgeoning Internet of Things.
The main goal of SIU investigators is to develop testimony and evidence that helps validate or expose a claim or underwriting scenario, and move toward a conclusion. The enhanced collection and analysis of sensed data significantly improve investigations. Vehicle movement and location data hedge against drivers who falsely register and insure their vehicles in another state where auto premiums are lower.
Impact can be proven in false claims involving claimed vehicle damage and knockdowns of pedestrians. This helps determine the true victim.
Dishonest intent and exaggeration of “caused” or staged vehicle accidents also can be exposed by analyzing vehicle telematics that reveal braking, impact, speed and other collision measures.
Real-time video streaming on residential and commercial break-ins can lock in the true damage and accurate volume of stolen items. This reduces the claimant’s ability to inflate claims or invent stolen goods.
Would-be arson scams can fall prey to the same evidence.
Wearables. Here is another emerging area of sensed data. Wearable devices such as work vests are embedded with sensors that can alert for injury hot spots at a worksite.
Hard-hat zones and slippery footing areas are two tricky exposures. Sensed data can assist in verifying injury claims, and help detect fake work injuries with the accumulated measures of body movements.
Yet more fraud controls are being crafted into the claim process to better rule out falsified or manipulated data being used to support an inflated settlement. Doctored photos and forged documents are two examples. And the data from monitoring and sending devices used in validating claims also requires automated scrutiny by analytics to ensure accuracy.
Less fraud in the cards?
Fraud detection and customer service can thrive in a healthy symbiotic relationship. The primary driver is to offer the best possible service to honest customers: more accuracy, process transparency, two-way communication and prompt resolution.
As technology transforms data into shared and actionable intelligence, claims will be validated more promptly. The increased transparency will eliminate much of the unknown, including claimant anxiety.
Another important positive byproduct of intelligence-supported decisionmaking and claim transparency is to reduce the claimant’s opportunity and inclination for the bluff.
Having all the cards face-up can have a major impact on claimant behavior and attitudes. If all players in the game see all cards, they know there is a limited opportunity for falsehoods, and the negative consequences become clear.
“Increased transparency will eliminate much of the unknown — and claimant anxiety.”
Let’s flip over all the cards at the point of claim, and resolve the matter as quickly and accurately as possible. Loss reimbursement can begin while the engine coolant still is dripping onto the pavement.
Claim-history analysis combined with third-party dataset inclusion in real-time can quickly identify claims that must exit the high-speed conveyor and be routed for specialized handling such as complex loss adjustment, SIU investigation or legal review. The losses that remain on the fast track move quickly to indemnification.
The art of resource deployment also improves as complexity becomes evident with more intelligence developed at the point of claim. This helps the insurer route the claim to the adjuster or fraud investigator best suited to resolve the situation.
Now back to the symbiotic relationship: The new order validates legitimate claims faster, and more consistently. Let’s flip over all the cards at the point of claim, and resolve the matter as quickly and accurately as possible. Automobile loss reimbursement can begin while the engine coolant still is dripping onto the pavement.
Honest losses are more easily recognized and more promptly honored. It’s a win-win at the card table of insurance claims.
About the author: Thomas Mulvey is Assistant Vice President at ISO/Verisk.