Patent Issued for Utilizing machine learning models, predictive analytics, and data mining to identify a vehicle insurance fraud ring (USPTO 11562373): Accenture Global Solutions Limited

2023 FEB 13 (NewsRx) -- By a News Reporter-Staff News Editor at Network Business Daily -- Accenture Global Solutions Limited (Dublin, Ireland) has been issued patent number 11562373, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Contractor, Gopali Raval (Mumbai, IN), Lakshminarayanan, Subhashini (Chennai, IN), Ss, Shantanu (Mumbai, IN).

This patent was filed on August 6, 2020 and was published online on January 24, 2023.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Insurance fraud is an act committed to defraud an insurance process. For example, insurance fraud may occur when a claimant attempts to obtain some benefit or advantage the claimant is not entitled to, or when an insurance company knowingly denies some benefit that is rightfully due to a claimant.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “In some implementations, a method includes receiving, by a device, input data associated with vehicle insurance claims; consolidating and pre-processing, by the device, the input data to generate processed input data; processing, by the device, claims data, social media data, treatment data, and repair shop data, of the processed input data, with a long short-term memory model and a conditional random field model, to resolve ambiguities in the processed input data and to generate resolved data; processing, by the device, the resolved data, with a neural network model, to generate related data identifying relations between persons and vehicle accidents; performing, by the device, natural language processing on notes of claims adjusters and vehicle accident descriptions, of the resolved data, to extract feature data identifying features; processing, by the device, the feature data, with a convolutional neural network (CNN) model, to determine contradiction data identifying contradictions in the feature data; processing, by the device, weather data, location data, and telematics data, of the processed input data, with a graph-based entropy model, to determine actual weather conditions and actual locations associated with the vehicle accidents; processing, by the device, the related data, the contradiction data, and data identifying the actual weather conditions and the actual locations associated with the vehicle accidents, with a graph CNN model and a graph-based attention network model, to generate a knowledge graph; identifying, by the device and based on the knowledge graph, a fraud ring associated with two or more persons; and performing, by the device, one or more actions based on identifying the fraud ring.

“In some implementations, a device includes one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: receive input data associated with vehicle insurance claims; process claims data, social media data, treatment data, and repair shop data, of the input data, with a first model and a second model, to resolve ambiguities in the input data and to generate resolved data; process the resolved data, with a third model, to generate related data identifying relations between persons and vehicle accidents; perform natural language processing on notes of claims adjusters and vehicle accident descriptions, of the resolved data, to extract feature data identifying features; process the feature data, with a fourth model, to determine contradiction data identifying contradictions in the feature data; process weather data, location data, and telematics data, of the input data, with a fifth model, to determine actual weather conditions and actual locations associated with the vehicle accidents; process the related data, the contradiction data, and data identifying the actual weather conditions and the actual locations associated with the vehicle accidents, with a sixth model and a seventh model, to generate a knowledge graph; determine, based on the knowledge graph, a fraud ring associated with two or more persons; and perform one or more actions based on the fraud ring.

“In some implementations, a non-transitory computer-readable medium storing instructions includes one or more instructions that, when executed by one or more processors, cause the one or more processors to: receive input data associated with vehicle insurance claims; process claims data, social media data, treatment data, and repair shop data, of the input data, with a long short-term memory model and a conditional random field model, to resolve ambiguities in the input data and to generate resolved data; process the resolved data, with a neural network model, to generate related data identifying relations between persons and vehicle accidents; perform natural language processing on notes of claims adjusters and vehicle accident descriptions, of the resolved data, to extract feature data identifying features; process the feature data, with a CNN model, to determine contradiction data identifying contradictions in the feature data; process weather data, location data, and telematics data, of the input data, with a graph-based entropy model, to determine actual weather conditions and actual locations associated with the vehicle accidents; process the related data, the contradiction data, and data identifying the actual weather conditions and the actual locations associated with the vehicle accidents, with a graph CNN model and a graph-based attention network model, to generate a knowledge graph; identify, based on the knowledge graph, a fraud ring associated with two or more persons; utilize convolutional autoencoders to determine a reconstructed fraud ring; compare data identifying the fraud ring and data identifying the reconstructed fraud ring; and determine whether the fraud ring is valid based on comparing the data identifying the fraud ring and the data identifying the reconstructed fraud ring.”

The claims supplied by the inventors are:

“1. A method, comprising: receiving, by a device, input data associated with vehicle insurance claims; consolidating and pre-processing, by the device, the input data to generate processed input data; processing, by the device, claims data, social media data, treatment data, and repair shop data, of the processed input data, with a long short-term memory model and a conditional random field model, to resolve ambiguities in the processed input data and to generate resolved data, wherein processing the claims data, the social media data, the treatment data, and the repair shop data comprises: predicting classifications associated with entities identified in the claims data, the social media data, the treatment data, and the repair shop data based on determining a particular word within a threshold quantity of words, generating a co-resolution matrix based on the entities and the classifications, determining a value for the co-resolution matrix, based on a Levenshtein distance between a first named entity, of the entities, and a second named entity, of the entities, based on a minimum quantity of single character edits required to cause the first named entity and the second named entity to match, determining an ambiguity, of the ambiguities, exists between the first named entity and the second named entity based on the Levenshtein distance, and resolving the ambiguity based on replacing the first named entity or the second named entity with a common identifier; processing, by the device, the resolved data, with a neural network model, to generate related data identifying relations between persons and vehicle accidents; performing, by the device, natural language processing on notes of claims adjusters and vehicle accident descriptions, of the resolved data, to extract feature data identifying features; processing, by the device, the feature data, with a convolutional neural network (CNN) model, to determine contradiction data identifying contradictions in the feature data; processing, by the device, weather data, location data, and telematics data, of the processed input data, with a graph-based entropy model, to determine actual weather conditions and actual locations associated with the vehicle accidents; processing, by the device, the related data, the contradiction data, and data identifying the actual weather conditions and the actual locations associated with the vehicle accidents, with a graph CNN model and a graph-based attention network model, to generate a knowledge graph; identifying, by the device and based on the knowledge graph, a fraud ring associated with two or more persons; and performing, by the device, one or more actions based on identifying the fraud ring.

“2. The method of claim 1, wherein performing the one or more actions includes one or more of: utilizing one or more convolutional autoencoders to validate an identification of the fraud ring; causing one or more insurance claims associated with the fraud ring to be denied; or notifying one or more law enforcement agencies about the fraud ring.

“3. The method of claim 1, wherein performing the one or more actions includes one or more of: notifying one or more vehicle insurance companies about the fraud ring; causing one or more vehicles associated with the two or more persons of the fraud ring to be disabled; or retraining one or more of the long short-term memory model, the conditional random field model, the neural network model, the CNN model, the graph-based entropy model, the graph CNN model, or the graph-based attention network model based on data associated with the fraud ring.

“4. The method of claim 1, wherein the input data includes one or more of: the claims data identifying the vehicle insurance claims for the vehicle accidents, the social media data identifying social media interactions of parties associated with the vehicle accidents, the treatment data identifying treatment of the vehicle insurance claims, the repair shop data identifying repair shop costs associated with the vehicle insurance claims, the data identifying the actual weather conditions and the actual locations associated with the vehicle accidents, the weather data identifying weather associated with the vehicle accidents, the location data identifying geographical locations associated with the vehicle accidents, or the telematics data identifying telematics associated with the vehicle accidents.

“5. The method of claim 1, further comprising one or more of: performing data mining on the weather data to identify weather associated with the vehicle accidents; performing data mining on the location data to identify geographical locations associated with the vehicle accidents; or performing data mining on the telematics data to identify telematics associated with the vehicle accidents.

“6. The method of claim 1, wherein processing the claims data, the social media data, the treatment data, and the repair shop data, of the processed input data, with the long short-term memory model and the conditional random field model, to resolve the ambiguities in the processed input data and to generate the resolved data comprises: processing the claims data, the social media data, the treatment data, and the repair shop data, of the processed input data, with the long short-term memory model and the conditional random field model, to identify persons involved in vehicle insurance claims; and processing data identifying the persons involved in the vehicle insurance claims, with a Levenshtein distance model and a bidirectional long short-term memory model, to resolve the ambiguities in the processed input data and to generate the resolved data.

“7. The method of claim 1, wherein the neural network model includes a graph neural network model based on an incidence matrix, an adjacency matrix, a degree matrix, and a Laplacian matrix.

“8. A device, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: receive input data associated with vehicle insurance claims; process claims data, social media data, treatment data, and repair shop data, of the input data, with a first model and a second model, to resolve ambiguities in the input data and to generate resolved data, wherein the one or more processors, to process the claims data, the social media data, the treatment data, and the repair shop data, are configured to: predict classifications associated with entities identified in the claims data, the social media data, the treatment data, and the repair shop data based on determining a particular word within a threshold quantity of words, generate a co-resolution matrix based on the entities and the classifications, determine a value for the co-resolution matrix, based on a Levenshtein distance between a first named entity, of the entities, and a second named entity, of the entities, based on a minimum quantity of single character edits required to cause the first named entity and the second named entity to match, determine an ambiguity, of the ambiguities, exists between the first named entity and the second named entity based on the Levenshtein distance, and resolve the ambiguity based on replacing the first named entity or the second named entity with a common identifier; process the resolved data, with a third model, to generate related data identifying relations between persons and vehicle accidents; perform natural language processing on notes of claims adjusters and vehicle accident descriptions, of the resolved data, to extract feature data identifying features; process the feature data, with a fourth model, to determine contradiction data identifying contradictions in the feature data; process weather data, location data, and telematics data, of the input data, with a fifth model, to determine actual weather conditions and actual locations associated with the vehicle accidents; process the related data, the contradiction data, and data identifying the actual weather conditions and the actual locations associated with the vehicle accidents, with a sixth model and a seventh model, to generate a knowledge graph; determine, based on the knowledge graph, a fraud ring associated with two or more persons; and perform one or more actions based on the fraud ring.

“9. The device of claim 8, wherein the one or more processors are further configured to: perform natural language processing on the social media data to extract additional features for the feature data.

“10. The device of claim 8, wherein the feature data includes one or more of: nouns connected to a query context, verbs connected to the query context, adverbs connected to the query context, adjectives connected to the query context, or phrases connected to the query context.

“11. The device of claim 8, wherein the one or more processors, when processing the feature data, with the fourth model, to determine the contradiction data identifying the contradictions in the feature data, are configured to: process the feature data associated with the notes of the claims adjusters, with the fourth model, to determine first meanings associated with the notes of the claims adjusters; process the feature data associated with the vehicle accident descriptions, with the fourth model, to determine second meanings associated with the vehicle accident descriptions; and compare the first meanings and the second meanings to determine the contradiction data identifying the contradictions in the feature data.”

There are additional claims. Please visit full patent to read further.

For the URL and additional information on this patent, see: Contractor, Gopali Raval. Utilizing machine learning models, predictive analytics, and data mining to identify a vehicle insurance fraud ring. U.S. Patent Number 11562373, filed August 6, 2020, and published online on January 24, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11562373)&db=USPAT&type=ids

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