Financial Data Migration Anomaly Detection With User Clustering
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Solution Overview
Problem
Existing data management systems face challenges in efficiently and effectively detecting issues with new data acquisition processes for individual users, leading to incomplete or erroneous data acquisition, which can result in user frustration and resource-intensive manual troubleshooting.
Innovation Solution
Implement a machine learning-based analysis model to compare data from both old and new data acquisition processes, using supervised learning to identify discrepancies, and a clustering model to group users with similar issues, enabling targeted troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual investigation by experts is used to detect issues with new data acquisition processes, then detection accuracy is improved, but resource consumption and time expenditure increase enormously
Solution Approach 1:
The patent replaces manual expert investigation with an automated machine learning-based analysis model. The system automatically compares data from old and new data acquisition processes, using supervised learning to identify discrepancies and detect issues without human intervention, thereby maintaining detection accuracy while dramatically reducing resource consumption.
Solution Approach 2:
The system enables self-service by automatically detecting and identifying issues with the new data acquisition process without requiring expert intervention. The analysis model autonomously compares data streams, identifies problems, and provides diagnostic information, allowing the system to self-diagnose migration issues.
2Reliability
If each user's data stream is manually checked to detect acquisition issues, then detection completeness is improved, but the number of detectable users is limited by available expert resources
Solution Approach 1:
The analysis model serves multiple functions: it detects issues across all user data streams simultaneously, clusters users with similar problems, identifies the root causes of acquisition failures, and provides diagnostic information. This universal system can scale to handle any number of users without requiring additional expert resources.
Solution Approach 2:
The patent segments users into clusters based on similarity of their data acquisition issues. By grouping users with comparable problems, the system can efficiently analyze and resolve issues across the entire user base, improving both detection completeness and scalability.
3Extent of automation
If traditional automated testing is implemented without considering data structure differences, then automation level is improved, but detection accuracy decreases due to process-specific data variations
Solution Approach 1:
The analysis model is trained with process-specific data characteristics and structures. It learns to recognize the unique patterns and formats of data from different data acquisition processes, allowing it to accurately compare and detect issues while accounting for legitimate variations in data structure between processes.
Solution Approach 2:
The system adapts to different data structures by adjusting its analysis parameters based on the specific data acquisition process being evaluated. The machine learning model learns process-specific parameters and patterns, enabling accurate automated detection despite variations in data formats and structures across different processes.
Data Source
AI summary
A method and system determines whether or not a new data acquisition process is working for individual financial accounts of users of a data management system. The method and system trains an analysis model with a machine learning process. The trained analysis model then analyzes financial data obtained by both an old data acquisition process and the new data acquisition process. The trained analysis model identifies whether the new data acquisition process is working properly based on the analysis.