Claim Data Normalization for Fraud Irregularity Detection
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Solution Overview
Problem
Existing systems struggle with efficiently processing and detecting large datasets with heterogeneous data formats, codes, and industry-specific codes and industry-specific codes and industry-specific natural language, due to the complexity of handling vast and varied datasets, particularly in the insurance industry, where the system is not dynamically updated and compactly and efficiently presenting the data, with the system is not dynamically updated and compactly and efficiently processing and detecting large datasets, particularly in the insurance industry, where the system is not dynamically detecting fraudulent claims.
Innovation Solution
The system employs a network of data ingestion, statistics, and user interface engines to process and visualize large datasets, dynamically updating user interfaces in response to user inputs, enabling efficient detection of fraudulent claims by standardizing data formats, calculating statistical measures, and providing interactive visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple heterogeneous sources is aggregated and processed, then the ability to detect irregularities improves, but the complexity of data standardization and processing increases
Solution Approach 1:
The patent introduces a data normalization layer that acts as an intermediary between heterogeneous data sources and the irregularity detection engine. This normalization layer standardizes codes, formats, and structures from multiple providers into a unified schema, enabling the detection system to process diverse claim data without being overwhelmed by format variability. The normalization layer translates provider-specific codes into standardized classifications while preserving the ability to trace back to original sources.
Solution Approach 2:
The system segments the complex data processing task into distinct modular components: data ingestion modules for different providers, a normalization layer for standardization, a aggregation engine for combining data, and an irregularity detection engine for analysis. Each module handles specific aspects of the processing pipeline independently, reducing overall system complexity while maintaining comprehensive irregularity detection capabilities across heterogeneous data sources.
2Productivity
If large volumes of claim data are processed rapidly, then processing speed improves, but the resources required for storage and computation increase
Solution Approach 1:
The patent implements selective aggregation and filtering mechanisms that process only the most relevant data subsets for irregularity detection. Rather than analyzing every single claim record in detail, the system applies preliminary filtering rules and aggregation techniques to identify suspicious patterns at higher levels of abstraction. This allows rapid processing of large datasets by focusing computational resources on potentially fraudulent claims rather than uniformly processing all data at full depth.
Solution Approach 2:
The system employs periodic batch processing combined with real-time streaming analysis. Large volumes of claim data are processed in periodic batches for comprehensive aggregation and normalization, while critical irregularity detection operations occur continuously in real-time streams. This hybrid approach balances resource consumption by intensive periodic processing with efficient continuous monitoring, maintaining high productivity without requiring excessive sustained computational resources.
Data Source
AI summary
A computer-implemented method is provided to provide analysis of claim information. The system may receive claim data from a plurality of entities and, for one or more claim items in the received claim data, determine a format of the claim item. The system may convert the claim item from the determined format into a standard format. The system may receive a selection of one or more providers, and determine one or more claim items associated with the provider in the selection. The system may generate a user interface, the user interface comprising a visual representation of one or more attributes of the one or more claim items.