Anomaly Detection Model for Data Record Scrutiny
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Solution Overview
Problem
Existing data management systems lack effective methods to identify and scrutinize anomalous data records, particularly in resource transfer transactions, which may lead to inaccurate or fraudulent approvals due to insufficient scrutiny.
Innovation Solution
A system and method that utilize a detection model based on score distribution representations and dynamic quantile weights to generate anomaly predictions for data records, identifying outliers by analyzing meta attributes and transmitting signals for further scrutiny before approval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data records are approved without sufficient scrutiny, then approval speed increases, but accuracy and reliability of approvals deteriorate
Solution Approach 1:
The system performs preliminary anomaly detection and risk assessment on data records before they reach the approval stage. By pre-identifying potentially fraudulent or anomalous records using detection models and scoring mechanisms, the system prepares flagged records for enhanced scrutiny, thereby maintaining high approval speed for normal records while ensuring reliability for suspicious ones.
Solution Approach 2:
The patent introduces an intermediary anomaly detection layer between data record submission and final approval. This intermediary system uses detection models, scoring mechanisms, and flagging to mediate the approval process, automatically routing high-risk records for additional review while allowing low-risk records to proceed quickly, thus resolving the contradiction between speed and accuracy.
2Reliability
If comprehensive scrutiny is applied to all data records, then approval accuracy improves, but processing time increases
Solution Approach 1:
The system applies different levels of scrutiny to different data records based on their individual risk profiles. High-risk records identified through anomaly detection receive comprehensive scrutiny, while low-risk records undergo minimal or automated review. This localized quality approach ensures high accuracy for critical records without wasting time on obviously safe ones.
Solution Approach 2:
The patent changes the scrutiny parameter dynamically based on anomaly scores and risk indicators. Records with high anomaly scores trigger intensive review protocols, while those with low scores receive expedited processing. This parameter-based differentiation allows the system to maintain high overall accuracy while minimizing average processing time.
3Measurement precision
If advanced detection models are implemented, then anomaly identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the anomaly detection system into modular components: data collection modules, detection model modules, scoring modules, flagging modules, and review routing modules. Each component performs a specific function and can be independently configured or updated. This segmentation maintains high detection accuracy while managing system complexity through modularity and clear separation of concerns.
Data Source
AI summary
Systems and methods of monitoring for anomalous data records. The system conducts a method including: receiving a data record associated with at least one meta attribute to determine whether subsequent processing of the data record is warranted; generating an anomaly prediction for the data record based on a detection model and the at least one meta attribute associated with the data record, the detection model defined by a plurality of score distribution representations based on quantile bins and a dynamic quantile weight for providing an interim anomaly measure corresponding to respective score distribution representations, wherein the anomaly prediction is generated based on a combination of interim anomaly measures associated with respective meta attributes associated with the data record; and transmitting a signal representing the anomaly prediction for presentation at a user device for identifying one or more data records for subsequent data processes.