Graph-Based Reconciliation for Multi-System Journal Entries
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Solution Overview
Problem
Conventional reconciliation systems face challenges in identifying and correcting complex issues with journal entries from multiple transactional systems, such as mis-specified accounting codes or entry dates, due to limited visibility and inefficient methods for matching transactions across different accounting systems.
Innovation Solution
A computer-implemented method and system that receives journal entries from multiple transactional systems, determines association scores, generates a reconciliation graph, recommends actions to minimize unconnected nodes, and updates the graph based on user feedback to optimize matching and minimize unexplained variance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional reconciliation systems use traditional matching methods, then the system structure remains simple, but the ability to identify and correct complex issues with journal entries from multiple transactional systems deteriorates
Solution Approach 1:
The patent segments the reconciliation process into distinct functional modules: journal entry receipt from multiple transactional systems, association score determination, graph generation, action recommendation, and feedback processing. Each module handles a specific aspect of the reconciliation task, enabling the system to manage complex multi-system reconciliation while maintaining clear separation of concerns and improving overall reliability
Solution Approach 2:
The patent introduces a graph-based intermediary structure that represents journal entries as nodes and potential matches as edges with association scores. This graph serves as a mediator between the raw journal entry data and the final reconciliation decisions, enabling the system to visualize and analyze complex relationships across multiple transactional systems in a structured manner
2Productivity
If conventional systems use manual reconciliation methods, then the system complexity remains low, but the productivity and efficiency of the reconciliation process deteriorates
Solution Approach 1:
The system implements self-service capabilities by automatically determining association scores between journal entries, generating recommended actions, and processing feedback without requiring manual intervention for each reconciliation task. The automated graph analysis and recommendation engine enable the system to handle large volumes of journal entries from multiple transactional systems efficiently, significantly improving productivity
Solution Approach 2:
The patent incorporates a feedback mechanism where user responses to recommended actions are processed and used to refine future recommendations. This feedback loop continuously improves the system's ability to identify correct matches, enhancing reconciliation efficiency over time while the system manages its own performance optimization
3Reliability
If the system processes all journal entries from multiple transactional systems, then the completeness of reconciliation data improves, but the time required for processing deteriorates
Solution Approach 1:
The patent changes the parameter representation of journal entries by converting them into graph nodes with associated attributes (association scores, transactional system identifiers, time stamps). This parameter transformation enables efficient graph-based algorithms to quickly analyze relationships across all journal entries from multiple systems, maintaining completeness while reducing processing time through optimized computational approaches
Data Source
AI summary
A computer-implemented method includes: receiving, by a computing device, a plurality of journal entries from a plurality of transactional systems; determining, by the computing device, association scores for a plurality of pairs of journal entries selected from the plurality of journal entries; generating, by the computing device, a reconciliation graph including a plurality of nodes representing the plurality of journal entries and a plurality of edges based on the determined association scores; recommending, by the computing device, actions to apply to at least one journal entry of the plurality of journal entries to minimize a number of nodes not connected by the plurality of edges in the reconciliation graph; receiving, by the computing device, feedback regarding the recommended actions to apply to the at least one journal entry; and updating, by the computing device, the reconciliation graph based on the feedback.