Self-Pairing Database Transaction Matching System
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Solution Overview
Problem
Traditional methods for matching business transaction records are inefficient due to the need for manual rule creation and updates, lack of automated validation for correct matches, and absence of systematic assessment of match confidence and optimization within the overall context of all potential matches, leading to potential mismatches and operational risks.
Innovation Solution
A system and method that utilize validated historical transaction matches to optimize the matching of new transactions by employing a classifier that decomposes each event match into independent causal pairs, calculates match probabilities, and selects causal pairs to maximize quality values, thereby minimizing operational risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual rule creation and updates are used for matching transaction records, then flexibility in handling different matching scenarios is maintained, but operational efficiency and productivity deteriorate due to time-consuming manual processes
Solution Approach 1:
The system automatically learns and creates matching rules by analyzing validated historical transaction matches, eliminating the need for manual rule creation and updates. The classifier self-trains on historical data and autonomously generates optimized matching rules for new transactions, making the system self-sufficient while maintaining high adaptability to different matching scenarios.
2Device complexity
If traditional matching methods are used without automated validation, then system complexity is reduced, but reliability deteriorates due to potential mismatches and operational risks
Solution Approach 1:
The system implements automated validation by comparing match results against learned patterns from historical validated matches. The classifier provides feedback on match confidence levels and validates each match against established criteria, automatically identifying and flagging potential mismatches while maintaining manageable system complexity through rule-based validation.
3Reliability
If match confidence assessment is performed systematically, then reliability of matching results is improved, but computational time and processing duration increase
Solution Approach 1:
The system performs systematic confidence assessment by evaluating matches against multiple criteria and historical patterns, but implements partial action by focusing computational resources on uncertain matches that require validation. High-confidence matches are processed quickly with minimal validation, while low-confidence matches receive more thorough assessment, optimizing the balance between reliability and processing time.
Data Source
AI summary
A method, system and program product comprise accessing a transaction records database. Unmatched records are collected into a first set. The first set at least comprises events and transactions. Probabilities of event matches of transactions originating from an event are calculated. The calculating uses at least defined features and stored probability distributions. A quality value for each of the event matches is calculated. The quality value is at least in part being determined by the probability of the event match. A second set of optimized event matches is determined using at least the quality values. Each of the optimized event matches at least comprises transactions deemed to have been generated by the event.