Data Normalization for Heterogeneous Claim Irregularity Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle with efficiently processing and analyzing large, heterogeneous datasets for irregularity detection, particularly in industries like insurance, due to varying data formats, codes, and industry-specific terminology, which complicates fraud detection and requires significant manual review.
Innovation Solution
A system that automates data ingestion, normalization, and aggregation, followed by interactive user interfaces for dynamic visualization and analysis, enabling efficient detection of irregularities and fraud through standardized data presentation and statistical measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple heterogeneous sources is ingested and aggregated, then the quantity and variety of analyzable data increases, but the complexity of data processing and standardization increases
Solution Approach 1:
The system segments the complex data processing task into distinct functional modules: data ingestion component, normalization component, aggregation component, and visualization component. Each module handles a specific aspect of processing heterogeneous data, making the overall system more manageable and maintainable while handling large quantities of diverse data
Solution Approach 2:
The patent introduces a normalization layer as an intermediary between raw heterogeneous data and the analysis/visualization components. This normalization component translates various data formats, codes, and terminologies into a standardized internal representation, facilitating seamless processing of data from multiple sources without increasing overall system complexity
2Measurement precision
If individual review of data items is performed, then detection precision of anomalies improves, but the time required for analysis increases
Solution Approach 1:
The system merges multiple data items into aggregate representations that preserve anomaly detection capability while reducing the total number of items requiring individual review. By combining related data elements and highlighting unusual patterns at the aggregate level, the system maintains detection precision for significant anomalies while dramatically reducing analysis time
Solution Approach 2:
Instead of reviewing all individual data items, the system applies partial action by focusing analytical resources only on aggregated data showing unusual patterns or deviations. This approach maintains detection precision for meaningful anomalies while avoiding the time cost of examining every single data point
3Ease of operation
If data is standardized and normalized, then ease of analysis improves, but the processing time for transformation increases
Solution Approach 1:
The system performs preliminary normalization and standardization of data during the ingestion phase, before analysis begins. By pre-processing data into a standardized format upfront, the system eliminates the need for repeated transformation operations during analysis, reducing overall processing time while maintaining ease of analysis
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.