Transaction Reconciliation Segregating Heterogeneous Data
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Solution Overview
Problem
Current reconciliation systems are unable to handle heterogeneous data effectively, leading to manual processing of exceptions when trying to match transactions across systems, as they rely on pre-defined fixed matching rules that do not accommodate diverse data sets.
Innovation Solution
A method and system for reconciling transactions iteratively by segregating data into homogeneous sets using configurable matching rules, user-defined functions, and residual transaction creation, allowing for dynamic rule application and reconciliation based on calculated values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-defined fixed matching rules are applied on the entire set of data, then the reconciliation process is simple and fast, but the system cannot handle heterogeneous data effectively
Solution Approach 1:
The patent segments the entire data set into multiple homogeneous data sets based on data characteristics. Each homogeneous data set is then processed with appropriate matching rules, allowing the system to maintain speed while handling heterogeneous data effectively through divided processing.
Solution Approach 2:
The system dynamically selects and applies different matching rules based on the type of homogeneous data set being processed. Instead of using fixed rules for all data, the system adapts the matching approach according to data characteristics, enabling effective handling of heterogeneous data while maintaining efficiency.
2Measurement precision
If a complete match is required between transaction sets, then the matching criteria is clear and definitive, but unmatched transactions must be manually processed as exceptions
Solution Approach 1:
The patent applies partial matching by allowing matches based on subsets of criteria rather than requiring complete matches across all fields. This partial matching approach reduces the number of exceptions that require manual processing while maintaining sufficient matching accuracy for reconciliation purposes.
Solution Approach 2:
The system changes matching parameters dynamically based on data types and reconciliation requirements. By adjusting matching strictness and criteria weights, the system achieves adequate matching accuracy without generating excessive exceptions that would require manual intervention.
3Ease of operation
If manual processing is used for exception resolution, then complete control over reconciliation is maintained, but the overall reconciliation time increases significantly
Solution Approach 1:
The system performs self-service by automatically resolving many exceptions through iterative reconciliation processes. Homogeneous data sets are reprocessed with adjusted parameters, and the system autonomously resolves mismatches without requiring manual intervention for every exception, thereby maintaining control while improving throughput.
Solution Approach 2:
The system implements feedback mechanisms where reconciliation results are analyzed and used to adjust subsequent processing. Exception patterns are fed back into the system to refine matching rules and parameters, enabling automatic resolution of recurring exceptions and reducing the need for manual processing while maintaining operational control.
Data Source
AI summary
A method for reconciling transactions iteratively by segregating data into homogeneous data sets. The method includes acquiring transactions from two or more systems and comparing with a set of configurable predefined matching rules. Applying, based on the result of the comparison, one or more rules on the acquired transactions. The transactions of the two or more systems are scanned and a user defined function is applied on the transactions of the two systems. A value of the user defined function is calculated. If the value of the user defined function is residual value, a configurable criteria is applied on the value of the user defined function. A residual transaction is created in one of the systems and the value of the user defined function is recalculated. If the value of the user defined function is null value, indicates the transactions are reconciled.