Automated Data Reconciliation with Dynamic Matching Rules
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Solution Overview
Problem
Current data reconciliation methods lack efficiency in matching and reporting transactions between different data sets, often requiring manual intervention and lacking automated processes for identifying and resolving discrepancies.
Innovation Solution
A system and method for reconciling records that involves determining reconciliation tasks, obtaining reconciliation rules, identifying matching and unmatched transactions based on these rules, and providing a graphical user interface for user input to resolve discrepancies, with the capability to automatically match transactions and generate reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data reconciliation methods are used to match transactions between different data sets, then data accuracy can be maintained through human judgment, but the process requires significant manual effort and time
Solution Approach 1:
The system enables self-service automated reconciliation by implementing intelligent matching algorithms that automatically compare transactions across different data sets using multiple criteria (amount, date, description, reference numbers). The system independently identifies matches and discrepancies without requiring manual intervention for each transaction, thereby maintaining data accuracy while dramatically reducing the time required for reconciliation processes.
2Productivity
If automated reconciliation processes are implemented to reduce manual effort, then processing speed increases, but the system complexity increases
Solution Approach 1:
The automated reconciliation system is segmented into distinct functional modules: data import module, matching engine module, discrepancy identification module, and reporting module. Each module performs a specific function in the reconciliation workflow, making the overall complex process manageable and maintainable. The matching engine itself is segmented into multiple matching criteria (amount matching, date matching, description matching) that can be independently configured and executed.
Solution Approach 2:
The reconciliation system is designed with multi-functionality to handle various types of transactions and data sets through a single unified platform. The system can reconcile different account types (bank accounts, credit cards, loans) and supports multiple matching strategies, making it adaptable to diverse reconciliation needs without requiring separate systems for each scenario.
3Measurement precision
If comprehensive matching criteria are applied to ensure accurate transaction matching, then matching precision improves, but the number of unmatched transactions increases
Solution Approach 1:
The matching criteria system is designed to be dynamic and hierarchical. The system first applies strict matching criteria (exact amount, date, and description matches) to ensure high precision for clear matches. For transactions that remain unmatched, the system dynamically adjusts by applying progressively less stringent criteria or alternative matching strategies, allowing the matching process to adapt its precision level based on the specific transaction characteristics and reconciliation context.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reconciliation data. The methods include actions of determining to perform a reconciliation task between transactions of a primary type and transactions of a secondary type, determining a reconciliation profile associated with the reconciliation task, obtaining reconciliation rules associated with the reconciliation profile from a reconciliation rule database, obtaining primary records of the primary type and secondary records of the secondary type based on the reconciliation profile, identifying matching transactions in the primary records and the secondary records and identifying unmatched transactions in the primary records and secondary records based on the reconciliation rules, providing an indication of the matched transactions and the unmatched transactions, receiving input specifying a match between an unmatched primary record transaction and an unmatched secondary record transaction, and identifying the unmatched primary record transaction and the unmatched secondary record transaction as matching.