Custom Loss Scenario Modeling for Insurance Fraud Detection
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Solution Overview
Problem
Advanced fraud detection models in insurance analytics are overly complex, making it difficult for special investigation units to provide understandable fraud scenarios and context, and often rely on incomplete data, leading to challenges in identifying questionable claims.
Innovation Solution
A system and method for computerized loss scenario modeling and data analytics that includes a database for claims data, a front-end processor for generating customizable loss scenario rules, and a primary claims analytics processor for implementing these rules into a production data flow process, using business rules to enhance fraud detection and provide intuitive claim analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated fraud detection models with multivariate random forest models and neural networks are used, then detection accuracy and complex fraud pattern identification are improved, but model complexity and difficulty of providing understandable fraud scenarios increase
Solution Approach 1:
The system segments the complex fraud detection model into multiple interpretable components: individual rule evaluations, weighted risk scores, and hierarchical scenario categorizations. Each component can be independently understood and explained, while collectively they provide comprehensive fraud detection capability.
Solution Approach 2:
The patent introduces an intermediary layer between the sophisticated multivariate model and the end user. This layer translates complex model outputs into business-friendly explanations, rule-based justifications, and scenario descriptions that are understandable to investigators without requiring expertise in machine learning algorithms.
2Reliability
If advanced predictive models with multiple variables are implemented, then fraud detection capability is improved, but ease of providing understandable fraud scenarios and context deteriorates
Solution Approach 1:
The system transforms the output parameters of advanced predictive models into different forms that are easier to interpret. Instead of raw probability scores from multivariate models, the system generates weighted risk factors, rule trigger explanations, and scenario-based narratives that maintain detection reliability while improving understandability.
Solution Approach 2:
The patent creates simplified copies or representations of complex model outcomes. Business rules serve as interpretable copies that mirror the decision logic of sophisticated models, allowing investigators to understand fraud scenarios through familiar rule-based explanations rather than complex algorithmic outputs.
3Reliability
If fraud detection models use data from individual insurance carriers only, then data privacy and security are maintained, but data completeness and analytical depth for newer business deteriorate
Solution Approach 1:
The system implements a multi-functional data architecture that can operate in different modes: using individual carrier data for cases requiring high privacy protection, and integrating industry-wide data when enhanced analytical depth is needed. This universal approach allows the same platform to serve both data security requirements and data completeness needs.
Data Source
AI summary
Systems and methods for computerized loss scenario modeling and data analytics are provided herein. The systems and methods provide customized claim loss analytics to identify questionable insurance claims that require further investigation and include a database for storing claims data related to one or more loss events, a front-end processor configured to generate a user interface with elements configured to allow a user to select one or more business rules and one or more data points for a custom loss scenario rule, and a primary claims analytics processor in communication with the front-end processor that implements the custom loss scenario rule into a production data flow process, thereby impacting the results of data analytics performed by the primary processor.