Claims Processing System Using Multi-Source Data Fusion for Fraud Detection
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Solution Overview
Problem
Current insurance claims processing systems face challenges in detecting fraud and subrogation potential claims due to reliance on manual analysis, which is time-consuming and prone to errors, and automated systems often miss unstructured data insights, leading to high false positives and increased investigation costs.
Innovation Solution
A system that combines data from multiple sources, removes noise from text data, and uses analytical techniques like text analysis, predictive modeling, link analysis, and social-media analysis to identify suspicion indicators, assigning scores to claims for fraud and subrogation potential, thereby providing a rapid and accurate detection mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis is used for claims processing, then detection accuracy may be maintained, but processing time increases and errors occur
Solution Approach 1:
The system segments claims processing into distinct analytical components: text analysis for unstructured data, predictive modeling for structured data, link analysis for relationship detection, and social-media analysis for external validation. Each segment handles specific aspects of fraud detection, allowing parallel processing while maintaining comprehensive analysis accuracy.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated computational systems. Multiple analytical techniques (text analysis, predictive modeling, link analysis) are substituted for human reviewer processes, enabling faster processing while maintaining or improving detection accuracy through consistent application of analytical rules and algorithms.
2Productivity
If automated systems are used for claims processing, then processing speed increases, but false positives increase and unstructured data insights are missed
Solution Approach 1:
The system merges multiple analytical techniques into a unified fraud detection platform. Text analysis, predictive modeling, link analysis, and social-media analysis are combined and applied together to evaluate each claim, cross-validating findings across different methods to reduce false positives while maintaining high processing speed.
Solution Approach 2:
The analytical system is designed with multi-functionality to handle both structured and unstructured data types. The same platform performs text analysis on narrative claims, predictive modeling on structured fields, link analysis on entity relationships, and social-media analysis on external sources, providing comprehensive validation that reduces false positives across diverse data types.
3Measurement precision
If comprehensive data analysis is performed, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The complex analytical system is segmented into four distinct modular components: text analysis module, predictive modeling module, link analysis module, and social-media analysis module. Each module handles specific analytical tasks independently, making the overall complex system manageable through clear separation of functions while maintaining comprehensive detection precision.
Solution Approach 2:
The system introduces an intermediary layer that coordinates the multiple analytical techniques. This intermediary manages data flow between different analysis modules, integrates their findings, and synthesizes results into unified fraud risk assessments, reducing the apparent complexity for users while maintaining comprehensive analysis precision.
4Loss of energy
If early detection is implemented, then financial losses are minimized, but more claims require investigation
Solution Approach 1:
The system applies partial action by prioritizing analysis of high-risk claims identified through preliminary screening. Rather than investigating all claims equally, the system focuses analytical resources on claims showing suspicious patterns detected through text analysis, predictive modeling, or link analysis, reducing the number of claims requiring full investigation while still minimizing financial losses through early detection of high-risk cases.
Solution Approach 2:
The system implements feedback mechanisms where detection results from one claim inform the analysis of subsequent claims. Patterns identified in investigated claims feed back into the predictive modeling and scoring systems, improving the accuracy of early detection for future claims and reducing the number of investigations needed while maintaining loss minimization effectiveness.
Data Source
AI summary
Systems and methods for insurance claims processing in an insurance industry are described. The method comprises combining extracted claims data from one or more data sources to obtain a consolidated claims record and removing noise from text data of the consolidated claims record to obtain a claim dataset. The claims data comprises a plurality of claims. Further, ascertaining one or more suspicion indicators in the plurality of claims based on an analytical technique. Further, assigning a score to each of the plurality of claims based on at least one scoring rule. The score is an indicative of a level of suspicion of a claim. Furthermore, detecting at least one of insurance claims fraud and subrogation potential claims based on the score assigned to each of the plurality of claims.