Automated Remittance Value Outlier Detection in Database Records
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Solution Overview
Problem
Existing systems for managing transaction records in databases face challenges in efficiently detecting and processing outlier data, which can lead to delays and inefficiencies in record processing.
Innovation Solution
An automated, pattern-based detection system is implemented to identify outlier data in database records. This system applies pattern detection to filter records, calculate values for specific fields, and identify records with data that falls outside expected ranges. The system then maps these records to batches and assigns them to categories for further processing based on predefined workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated pattern-based detection is implemented to identify outlier data, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the record processing workflow into distinct phases: initial filtering using pattern detection rules, outlier identification through value comparison, batch mapping, and category assignment. This segmentation allows automated detection to handle specific tasks efficiently while keeping the overall system architecture manageable and modular.
Solution Approach 2:
The patent introduces an intermediary outlier detection engine that acts as a mediator between the raw record processing system and the final processing workflow. This engine applies pattern-based rules and statistical comparisons to identify outliers, thereby automating the detection process without requiring complete system redesign, thus improving efficiency while controlling complexity.
2Loss of time
If outlier records are filtered and prioritized for review, then processing time is reduced, but measurement precision requirements increase
Solution Approach 1:
The system applies partial action by not attempting to detect all potential outliers with absolute precision, but rather using pattern-based rules and statistical thresholds to identify the most significant outliers. This approach reduces processing time by focusing on high-probability cases while accepting that some edge cases may require manual verification, thus balancing speed and accuracy.
Solution Approach 2:
The patent replaces manual review of all records with an automated mechanical system that uses pattern detection algorithms, statistical comparisons, and predefined rules to identify outliers. This substitution dramatically reduces processing time while maintaining adequate detection accuracy through systematic application of detection criteria, though it requires careful calibration of detection thresholds.
Data Source
AI summary
Embodiments are directed to adjusting values used for detection of outlier data in database records. According to one embodiment the records can be reviewed and outlier data stored in one or more of the records can be detected. Outlier data can be considered any data that falls above, below, and/or outside of a range from a value that is expected or which is normal for that data. Detecting the outlier data in the predetermined field can be based on a review process performed on the identified records. Results of a review process can either confirm or correct the identification of the records as including outlier data. If corrections are made, one or more records can be removed from the identified one or more records having outlier data in the predetermined filed based on the received results of the review process.