Systems and methods of utilizing unmanned vehicles to detect insurance claim buildup

• US 10,535,103 B1
• Filed: 09/18/2015
• Issued: 01/14/2020
• Est. Priority Date: 09/22/2014
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Tofte, Nathan L., Ryan, Timothy W., Baumann, Nathan W., Jacob, Michael Shawn, Lillie, Joshua David, Harvey, Brian N., Lyons, Roxane, Grant, Rosemarie Geier
• Primary Examiner(s): Milef, Elda G
• Application Number:
  US14/858,699
• Time in Patent Office: 1,579 Days
• CPC Class Codes: B64C 39/024 of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64D 47/08 Arrangements of cameras
• CPC Class Codes: B64U 10/00 Type of UAV
• CPC Class Codes: B64U 10/14 with four distinct rotor axes, e.g. quadcopters
• CPC Class Codes: B64U 2101/00 UAVs specially adapted for particular uses or applications
• CPC Class Codes: B64U 2101/30 for imaging, photography or videography
• CPC Class Codes: G01C 11/02 Picture taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 17/05 Geographic models
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/30232 Surveillance
• CPC Class Codes: G06T 2207/30236 Traffic on road, railway or crossing
• CPC Class Codes: G06T 2207/30252 Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 2215/16 Using real world measurements to influence rendering
• CPC Class Codes: G06T 7/00 Image analysis
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/75 : involving models
• CPC Class Codes: G06V 10/42 : Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
• CPC Class Codes: G06V 20/10 : Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 20/41 : Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: G06V 20/44 : Event detection
• CPC Class Codes: H04N 5/44 : Receiver circuitry for the reception of television signals according to analogue transmission standards H04N5/14 takes precedence
• CPC Class Codes: H04N 7/185 : from a mobile camera, e.g. for remote control

Abstract

The present embodiments relate to detecting fraudulent insurance claims. According to certain aspects, a central monitoring server may receive and examine data detected by at least one unmanned vehicle and generate an estimated insurance claim for a loss event. The central monitoring server may then receive an actual insurance claim relating to the loss event, and may compare the estimated insurance claim to the actual insurance claim to identify potential buildup included in the actual insurance claim. If buildup is detected, the central monitoring server may then process the actual insurance claim accordingly based upon the potential buildup. As a result, claim monies may be paid to insureds that more accurately reflect actual losses resulting from the loss event, and insurance cost savings may be ultimately passed onto typical insurance customers.

Claims

1. A computer-implemented method of fraudulent claim detection, the method comprising:

directing, by a server associated with an insurance provider, at least one unmanned vehicle to a location of at least one of an individual or an insured asset to collect data indicating a loss event involving the individual or the insured asset;

receiving, at the server, the data detected by the at least one unmanned vehicle;

examining, by a processor, the data detected by the at least one unmanned vehicle to calculate an estimated amount of damage resulting from the loss event;

generating, by the processor, an estimated insurance claim for the loss event based upon the estimated amount of damage resulting from the loss event;

receiving, at the server, an actual insurance claim related to the loss event and submitted by a claimant individual;

comparing, by the processor, the estimated insurance claim to the actual insurance claim to identify potential buildup included in the actual insurance claim; and

processing the actual insurance claim based upon the potential buildup.

2. The computer-implemented method of claim 1, wherein receiving the data detected by the at least one unmanned vehicle comprises:

receiving at least one of image data, video data, and audio data detected by the at least one unmanned vehicle.

3. The computer-implemented method of claim 1, wherein examining the data detected by the at least one unmanned vehicle comprises:

analyzing the data detected by the at least one unmanned vehicle, and

based upon the analyzing, identifying at least one condition associated with the insured asset.

4. The computer-implemented method of claim 3, wherein identifying the at least one condition associated with the insured asset comprises:

identifying at least one of;

damage to the insured asset due to the loss event,

pre-existing damage to the insured asset prior to the loss event,

a required repair for the insured asset,

a location of the insured asset,

an orientation of the insured asset relative to a roadway, and

an operating condition at the time of the loss event.

5. The computer-implemented method of claim 1, wherein examining the data detected by the at least one unmanned vehicle comprises:

identifying at least one of;

an expected type of vehicle,

an expected number of passengers,

at least one expected injury of the passengers, and

at least one expected type of medical treatment for the passengers.

6. The computer-implemented method of claim 1, wherein examining the data detected by the at least one unmanned vehicle comprises:

accessing telematics information corresponding to at least one operating condition of the insured asset around a time of the loss event; and

analyzing the telematics information.

7. The computer-implemented method of claim 1, wherein examining the data detected by the at least one unmanned vehicle comprises:

determining a percentage fault for the loss event for at least one of;

at least one human driver,

at least one autonomous or semi-autonomous vehicle,

at least one road condition,

at least one traffic condition,

at least one weather condition, and

road construction.

8. The computer-implemented method of claim 1, wherein generating the estimated insurance claim for the loss event comprises:

populating the estimated insurance claim for the loss event with at least one of;

an estimated total monetary claim amount,

an estimated total repair amount, and

an estimated total medical treatment amount.

9. The computer-implemented method of claim 1, wherein generating the estimated insurance claim for the loss event comprises:

accessing historical data associated with previously-submitted insurance claims; and

generating the estimated insurance claim for the loss event based upon the estimated amount of damage resulting from the loss event and the historical data.

10. The computer-implemented method of claim 1, wherein comparing the estimated insurance claim to the actual insurance claim to identify the potential buildup included in the actual insurance claim comprises:

comparing at least one field of the estimated insurance claim with at least one corresponding field of the actual insurance claim, the at least one field including at least one of;

a total monetary amount,

a total repair amount,

a type of repair,

a total medical treatment amount,

a number of passengers, and

a type of medical treatment; and

identifying the potential buildup if the at least one field of the estimated insurance claim differs from the at least one corresponding field of the actual insurance claim by a predetermined threshold.

11. The computer-implemented method of claim 1, wherein processing the actual insurance claim based upon the potential buildup comprises:

processing the actual insurance claim by at least one of;

flagging the actual insurance claim for further review, and

forwarding the actual insurance claim to a fraud investigations team associated with the insurance provider.

12. A central monitoring server for detecting fraudulent claims, the system comprising:

a transceiver adapted to interface with and receive data detected by at least one unmanned vehicle;

a memory adapted to store non-transitory computer executable instructions; and

a processor adapted to interface with the transceiver and the memory, wherein the processor is configured to execute the non-transitory computer executable instructions to cause the processor to;

direct the at least one unmanned vehicle to a location of at least one of an individual or an insured asset to collect data indicating a loss event involving the individual or the insured asset;

receive, via the transceiver, the data detected by the at least one unmanned vehicle;

examine the data detected by the at least one unmanned vehicle to calculate an estimated amount of damage resulting from the loss event;

generate an estimated insurance claim for the loss event based upon the estimated amount of damage resulting from the loss event;

receive an actual insurance claim related to the loss event and submitted by a claimant individual;

compare the estimated insurance claim to the actual insurance claim to identify potential buildup included in the actual insurance claim; and

process the actual insurance claim based upon the potential buildup.

13. The central monitoring system of claim 12, wherein to receive the data detected by the at least one unmanned vehicle, the processor is configured to:

receive at least one of image data, video data, and audio data detected by the at least one unmanned vehicle.

14. The central monitoring server of claim 12, wherein to examine the data detected by the at least one unmanned vehicle, the processor is configured to:

analyze the data detected by the at least one unmanned vehicle; and

based upon the analysis, identify at least one condition associated with the insured asset.

15. The central monitoring server of claim 14, wherein to identify the at least one condition associated with the insured asset, the processor is configured to:

identify at least one of;

damage to the insured asset due to the loss event,

pre-existing damage to the insured asset prior to the loss event,

a required repair for the insured asset,

a location of the insured asset,

an orientation of the insured asset relative to a roadway, and

an operating condition at the time of the loss event.

16. The central monitoring server of claim 12, wherein to examine the data detected by the at least one unmanned vehicle, the processor is configured to:

identify at least one of;

an expected type of vehicle,

an expected number of passengers,

at least one expected injury of the passengers, and

at least one expected type of medical treatment for the passengers.

17. The central monitoring server of claim 12, wherein to examine the data detected by the at least one unmanned vehicle, the processor is configured to:

access telematics information corresponding to at least one operating condition of the insured asset around a time of the loss event; and

analyze the telematics information.

18. The central monitoring server of claim 12, wherein to examine the data detected by the at least one unmanned vehicle, the processor is configured to:

determine a percentage fault for the loss event for at least one of;

at least one human driver,

at least one autonomous or semi-autonomous vehicle,

at least one road condition,

at least one traffic condition,

at least one weather condition, and

road construction.

19. The central monitoring server of claim 12, wherein to generate the estimated insurance claim for the loss event, the processor is configured to:

populate the estimated insurance claim for the loss event with at least one of;

an estimated total monetary claim amount,

an estimated total repair amount, and

an estimated total medical treatment amount.

20. The central monitoring server of claim 12, wherein to generate the estimated insurance claim for the loss event, the processor is configured to:

access historical data associated with previously-submitted insurance claims; and

generate the estimated insurance claim for the loss event based upon the estimated amount of damage resulting from the loss event and the historical data.

21. The central monitoring server of claim 12, wherein to compare the estimated insurance claim to the actual insurance claim to identify the potential buildup included in the actual insurance claim, the processor is configured to:

compare at least one field of the estimated insurance claim with at least one corresponding field of the actual insurance claim, the at least one field including at least one of;

a total monetary amount,

a total repair amount,

a type of repair,

a total medical treatment amount,

a number of passengers, and

a type of medical treatment; and

identify the potential buildup if the at least one field of the estimated insurance claim differs from the at least one corresponding field of the actual insurance claim by a predetermined threshold.

22. The central monitoring server of claim 12, wherein to process the actual insurance claim based upon the potential buildup, the processor is configured to:

process the actual insurance claim by at least one of;

flagging the actual insurance claim for further review, and

forwarding the actual insurance claim to a fraud investigations team associated with the insurance provider.