Machine Learning Knowledge Graph for Vehicle Insurance Fraud Ring Detection
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Solution Overview
Problem
Insurance companies face challenges in efficiently identifying and addressing vehicle insurance fraud, particularly when multiple fraudulent claims are submitted by coordinated groups, leading to wasted resources and failure to detect entire fraud rings.
Innovation Solution
A system utilizing machine learning models, predictive analytics, and data mining processes input data from various sources, including claims, social media, treatment, and telematics, to generate a knowledge graph that identifies fraud rings by resolving ambiguities, extracting features, and determining relationships and contradictions, thereby enabling the detection of patterns across multiple claims.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional individual claim review methods are used, then each claim can be processed independently, but fraud rings involving multiple coordinated fraudulent claims cannot be detected
Solution Approach 1:
The patent segments the fraud detection process into multiple specialized machine learning models, each handling specific aspects: LSTM for sequential claim patterns, CRF for structured data relationships, CNN for image analysis, and NLP for text processing. This segmentation allows the system to detect fraud rings by analyzing individual claim components while maintaining overall system manageability
Solution Approach 2:
The patent merges multiple data sources and processing approaches into a unified fraud detection system. It combines structured claims data, unstructured text descriptions, image data, and external data sources into a comprehensive analysis framework that can identify coordinated fraud patterns across multiple claims
2Reliability
If comprehensive data from multiple sources is analyzed to identify fraud rings, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary data processing and feature extraction before the main fraud detection analysis. It pre-processes claims data, extracts relevant features using NLP, and prepares data structures in advance, which reduces the computational burden during actual fraud ring detection and accelerates processing time
3Measurement precision
If multiple machine learning models are used to process different data types, then detection precision improves, but model complexity and computational requirements increase
Solution Approach 1:
The patent applies different machine learning models with specialized capabilities to specific data types: LSTM for temporal sequential data, CRF for structured relational data, CNN for spatial image data, and NLP for unstructured text. This local quality approach ensures each data type is processed by the most appropriate model, improving precision while managing complexity through targeted specialization
4Measurement precision
If ambiguous data in claims is resolved through multiple processing models, then data accuracy improves, but processing complexity increases
Solution Approach 1:
The patent introduces an intermediary processing layer that resolves ambiguities in claims data before feeding it to the fraud detection models. This intermediary layer uses CRF models to disambiguate structured data and NLP techniques to clarify unstructured text, ensuring high data accuracy while isolating the complexity of ambiguity resolution from the main detection algorithms
Data Source
AI summary
A device may consolidate the input data associated with vehicle insurance claims to generate processed input data. The device may process claims data, treatment data, and repair shop data to resolve ambiguities in the processed input data and to generate resolved data. The device may process the resolved data to generate related data identifying relations between persons and vehicle accidents. The device may process notes of claims adjusters and vehicle accident descriptions to extract feature data identifying features. The device may process the feature data to determine contradiction data identifying contradictions in the feature data. The device may process weather data, location data, and telematics data to determine weather conditions and locations associated with the accidents. The device may process the related data, the contradiction data, the weather conditions, and the locations to generate a knowledge graph. The device may identify a fraud ring based on the knowledge graph.