Utilizing machine learning models, predictive analytics, and data mining to identify a vehicle insurance fraud ring

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;
• 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.

A non-transitory computer-readable medium storing instructions, the instructions comprising:

• 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 convolutional neural network (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.