Fraud Detection via Multipronged AI Model Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods are inadequate in identifying fraudulent healthcare providers effectively, leading to significant financial losses in the healthcare industry, with existing systems struggling to accurately detect anomalies in healthcare claims data.
Innovation Solution
A multipronged artificial intelligence (AI) model is employed, comprising a decision enhancer model, a trained claim evaluator model, and a provider anomaly measure model, which processes raw claims data to determine scores for each healthcare provider, including a decision enhancer score, denial risk score, and provider anomaly measure risk score, ultimately calculating a final provider-level risk score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single detection model is used for identifying fraudulent healthcare providers, then the system complexity is low, but the detection accuracy and reliability are insufficient
Solution Approach 1:
The fraud detection system is segmented into three specialized models: a decision enhancer model for claim-level anomaly detection, a trained claim evaluator model for provider-level assessment, and a provider anomaly measure model for comprehensive risk scoring. Each model focuses on specific aspects of fraud detection, improving overall reliability while maintaining manageable complexity through modular design
Solution Approach 2:
The system merges the outputs of three separate specialized models into a unified fraud detection framework. The decision enhancer score, claim evaluator score, and provider anomaly measure score are integrated to produce a comprehensive risk assessment, combining the strengths of multiple detection approaches to achieve higher reliability
2Reliability
If multiple AI models are employed to improve fraud detection accuracy, then the detection reliability improves, but the computational resources and processing time increase
Solution Approach 1:
The computational workload is segmented across three specialized models, each optimized for specific detection tasks. This segmentation allows for more efficient resource utilization compared to a single monolithic model, as each component can be tuned and executed optimally for its specific function
Solution Approach 2:
The system applies partial action by processing claims through multiple specialized models only when necessary for comprehensive risk assessment. The modular architecture allows selective deployment and optimization of computational resources based on the specific detection needs of different healthcare providers and claim types
3Measurement precision
If comprehensive multi-model analysis is performed on all claims data, then the detection precision improves, but the processing time and productivity are reduced
Solution Approach 1:
The claims processing system is segmented into multiple specialized models that can operate in parallel or selectively. The decision enhancer model processes claim-level anomalies, the trained claim evaluator model assesses provider patterns, and the provider anomaly measure model generates comprehensive scores. This segmentation enables precision through specialized analysis while improving productivity through parallel processing capabilities
Solution Approach 2:
The system applies partial action by not requiring all three models to process every single claim. Instead, the modular architecture allows selective application of detection models based on risk thresholds, claim characteristics, and provider history, maintaining high precision for suspicious cases while improving overall processing throughput
Data Source
AI summary
A system and computer-implemented method for identifying fraudulent healthcare providers receives raw claims data from one or more data sources. The raw claims data includes claims associated with a selected healthcare provider. Each of the claims includes one or more claim lines. A first model is executed on the raw claims data. The first model determines a first score for the healthcare provider. A second model is executed on the raw claims data. The second model determines a second score for the healthcare provider. In addition, a third model is executed on the raw claims data. The third model determines a third score for the healthcare provider. A final provider-level risk score is determined for the healthcare provider based on the first, second, and third scores.