Method and system for comparing automatically determined crash information to historical collision data to detect fraud

• US 9,904,928 B1
• Filed: 02/20/2015
• Issued: 02/27/2018
• Est. Priority Date: 07/11/2014
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Leise, William J.
• Primary Examiner(s): Ebersman, Bruce I
• Application Number:
  US14/627,104
• Time in Patent Office: 1,103 Days
• Field of Search: 705 4
• CPC Class Codes: B60W 2050/021 Means for detecting failure or malfunction
• CPC Class Codes: B60W 50/0205 Diagnosing or detecting failures; Failure detection models
• CPC Class Codes: G01B 21/00 Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
• CPC Class Codes: G01M 17/00 Testing of vehicles testing fluid tightness G01M3/00; testing elastic properties of bodies or chassis, e.g. torsion-testing, G01M5/00; testing alignment of vehicle headlight devices G01M11/06; testing of engines G01M15/00
• CPC Class Codes: G01S 5/06 Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements G01S5/12 takes precedence
• CPC Class Codes: G05B 23/0275 Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
• CPC Class Codes: G06Q 10/08355 Routing methods
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 30/0185 Product, service or business identity fraud
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 30/0625 Directed, with specific intent or strategy
• CPC Class Codes: G06Q 30/0639 Item locations
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G07C 5/00 Registering or indicating the working of vehicles
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0825 using optical means
• CPC Class Codes: G07C 5/0841 : Registering performance data recording measured values G01D; information storage G11B
• CPC Class Codes: H04W 4/80 : Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Abstract

To detect a risk of insurance fraud in a vehicle insurance claim for physical injuries, injury data may be retrieved from previous crashes involving similar type vehicles as the damaged vehicle, and having similar crash characteristics. A likelihood that the claimant suffered physical injuries from the crash may be determined based on the percentage of people who suffered similar injuries according to the injury data. If the likelihood is very small, the claim for physical injuries may be flagged as a risk of fraud.

Claims

1. A method for detecting potential fraud in vehicle insurance claims, the method executed by one or more processors programmed to perform the method, the method comprising:

receiving, at one or more processors, identification information for a damaged vehicle and sensor data captured during a vehicle crash involving the damaged vehicle including at least one of;

vehicle part position data from spatial sensors disposed within the damaged vehicle, vehicle acceleration data from an accelerometer disposed within the damaged vehicle, vehicle velocity data from a positioning device disposed within the damaged vehicle, or vehicle direction data from the positioning device;

receiving, at the one or more processors, an indication of an injury for a person involved in the vehicle crash with the damaged vehicle, including a location of the person with respect to the damaged vehicle when the injury occurred and at least one of;

an indication of a type of the injury or an indication of a severity of the injury;

analyzing, by the one or more processors, the identification information and the sensor data to identify a matching set of historical collision data for a same type of vehicle as the damaged vehicle, wherein the matching set of historical collision data includes historical injury data;

determining, by the one or more processors, one or more likelihoods that at least one of;

the received type of the injury occurred or the received severity of the injury occurred from the location of the person with respect to the damaged vehicle based on a subset of the historical injury data corresponding to the matching set of historical collision data for injuries occurring at the location of the person with respect to the damaged vehicle;

comparing, by the one or more processors, each of the one or more determined likelihoods to one of a plurality of predetermined threshold likelihoods;

when each determined likelihood is less than the respective predetermined threshold likelihood, generating, by the one or more processors, a suspicious loss indicator for the injury based on the likelihood that the received injury occurred; and

transmitting, by the one or more processors via a communication network, the suspicious loss indicator to a mobile computing device for display to flag the injury as a risk of fraud.

2. The method of claim 1, wherein historical injury data includes at least one of:

a number of people injured in past collisions involving the same type of vehicle having matching crash information with the damaged vehicle,

a number of people having a particular type of injury in past collisions involving the same type of vehicle having matching crash information with the damaged vehicle,

a number of people having a particular injury severity from a set of injury severities in past collisions involving the same type of vehicle having matching crash information with the damaged vehicle, wherein the set of injury severities includes minor, moderate, and severe, or

a number of people injured in a particular location from a set of locations in past collisions involving the same type of vehicle having matching crash information with the damaged vehicle, wherein the set of locations includes a driver seat in the same type of vehicle, a passenger seat in the same type vehicle, a driver seat in another vehicle which collided with the same type of vehicle, and a passenger seat in the other vehicle which collided with the same type of vehicle.

3. The method of claim 2, wherein determining a likelihood that the received type or severity of the injury occurred based on the historical injury data comprises determining, by the one or more processors, a percentage of people who were injured in past collisions involving the same type of vehicle having matching crash information with the damaged vehicle based on the historical injury data.

4. The method of claim 1, wherein receiving an indication of an injury further comprises receiving, at the one or more processors,

an indication of medical expenses associated with the injury.

5. The method of claim 1, wherein the location of the person involved in the vehicle crash with the damaged vehicle includes at least one of:

a driver seat in the damaged vehicle,

a passenger seat in the damaged vehicle,

a driver seat in another vehicle which collided with the damaged vehicle, or

a passenger seat in the other vehicle which collided with the damaged vehicle.

6. The method of claim 1, wherein generating the suspicious loss indicator further comprises determining, by the one or more processors, a suspicious loss category from a set of suspicious loss categories based on the likelihood that the received injury occurred including high risk, medium risk, and low risk.

7. A system for detecting potential fraud in vehicle insurance claims, the system comprising:

a communication network; and

one or more computing devices communicatively coupled to the communication network, each of the one or more computing devices having a memory and one or more processors and at least one of the computing device configured to;

receive identification information for a damaged vehicle and sensor data captured during a vehicle crash involving the damaged vehicle including at least one of;

vehicle part position data from spatial sensors disposed within the damaged vehicle, vehicle acceleration data from an accelerometer disposed within the damaged vehicle, vehicle velocity data from a positioning device disposed within the damaged vehicle, or vehicle direction data from the positioning device via the communication network,

receive an indication of an injury for a person involved in the vehicle crash with the damaged vehicle via the communication network, including a location of the person with respect to the damaged vehicle when the injury occurred and at least one of;

an indication of a type of the injury or an indication of a severity of the injury,

analyze the identification information and the sensor data to identify a matching set of historical collision data for a same type of vehicle as the damaged vehicle, wherein the matching set of historical collision data includes historical injury data,

determine one or more likelihoods that at least one of;

the received type of injury occurred or the received severity of the injury occurred from the location of the person with respect to the damaged vehicle based on a subset of the historical injury data corresponding to the matching set of historical collision data for injuries occurring at the location of the person with respect to the damaged vehicle,

compare each of the one or more determined likelihoods to one of a plurality of predetermined threshold likelihoods,

when each determined likelihood is less than the respective predetermined threshold likelihood, generate a suspicious loss indicator for the injury based on the likelihood that the received injury occurred; and

transmit, via the communication network, the suspicious loss indicator to a mobile computing device for display to flag the injury as a risk of fraud.

8. The system of claim 7, wherein historical injury data includes at least one of:

a number of people injured in past collisions involving the same type of vehicle having matching crash information with the damaged vehicle,

a number of people having a particular type of injury in past collisions involving the same type of vehicle having matching crash information with the damaged vehicle,

a number of people having a particular injury severity from a set of injury severities in past collisions involving the same type of vehicle having matching crash information with the damaged vehicle, wherein the set of injury severities includes minor, moderate, and severe, or

a number of people injured in a particular location from a set of locations in past collisions involving the same type of vehicle having matching crash information with the damaged vehicle, wherein the set of locations includes a driver seat in the same type of vehicle, a passenger seat in the same type vehicle, a driver seat in another vehicle which collided with the same type of vehicle, and a passenger seat in the other vehicle which collided with the same type of vehicle.

9. The system of claim 8, wherein to determine a likelihood that the received type or severity of the injury occurred based on the historical injury data, the at least one computing device is configured to determine a percentage of people who were injured in past collisions involving the same type of vehicle having matching crash information with the damaged vehicle based on the historical injury data.

10. The system of claim 7, wherein to receive an indication of an injury, the at least one computing device is further configured to receive:

an indication of medical expenses associated with the injury.

11. The system of claim 7, wherein the location of the person involved in the vehicle crash with the damaged vehicle includes at least one of:

a driver seat in the damaged vehicle,

a passenger seat in the damaged vehicle,

a driver seat in another vehicle which collided with the damaged vehicle, or

a passenger seat in the other vehicle which collided with the damaged vehicle.

12. The system of claim 7, wherein to generate the suspicious loss indicator, the at least one computing device is further configured to determine a suspicious loss category from a set of suspicious loss categories based on the likelihood that the received injury occurred including high risk, medium risk, and low risk.