Machine Learning Transaction Reconciliation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The process of trade reconciliation is time-consuming, error-prone, and wasteful of resources due to the manual handling of trade breaks and mismatched transaction entries, leading to potential errors and unreconciled transactions.
Innovation Solution
A machine learning system that classifies transaction entries into matched and unmatched sets, updates a transaction grouping model based on historical data, and uses this model to automatically group and reconcile unmatched entries, reducing manual intervention and increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual handling of trade breaks and mismatched transaction entries is used, then flexibility in handling complex cases is maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical reconciliation processes with an automated machine learning system that uses algorithms to match transaction entries across different books. The system automatically identifies and reconciles trades by comparing transaction data, eliminating the need for manual intervention while improving both speed and accuracy.
Solution Approach 2:
The reconciliation system performs self-service by automatically identifying matched and unmatched transaction entries, grouping unmatched entries using machine learning models, and resolving trade breaks without requiring manual handling. The system serves itself by continuously learning from historical data to improve its matching accuracy.
2Productivity
If automated matching systems are implemented, then reconciliation speed increases, but the system becomes complex and requires extensive training data
Solution Approach 1:
The patent segments the reconciliation process into distinct components: an initial matching model for quick automated matching, a machine learning model for handling unmatched entries, and a feedback mechanism for continuous improvement. This segmentation allows the system to process the majority of transactions automatically while directing only complex cases to the more sophisticated ML model.
Solution Approach 2:
The system performs preliminary automated matching using simpler, faster algorithms before applying more complex machine learning models. This preliminary action filters out the majority of easily matchable transactions, allowing the complex ML system to focus only on the difficult cases and reducing overall system complexity.
3Measurement precision
If more transaction data is analyzed to improve matching accuracy, then reconciliation precision increases, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by using different levels of analysis for different transaction entries. Easily matchable transactions receive minimal processing with basic matching criteria, while only unmatched or complex entries undergo extensive analysis using the full machine learning model with all available features and historical data.
Solution Approach 2:
The system segments transaction processing into multiple stages with increasing computational intensity. The first stage uses lightweight matching with minimal data analysis, the second stage applies machine learning to unmatched entries, and the third stage uses full historical data analysis only for remaining difficult cases, optimizing resource consumption at each level.
Data Source
AI summary
A device may receive transaction data associated with transactions. The transaction data may be associated with transaction entries that are associated with the transactions. The device may process, using a matching model, the transaction entries to classify the transaction entries into a set of matched transaction entries and a set of unmatched transaction entries. The device may update a transaction grouping model based on the set of matched transaction entries to create an updated transaction grouping model. The device may determine, using the updated transaction grouping model, that a subset of the set of unmatched transaction entries are associated with a same transaction. The device may classify the subset of the set of unmatched transaction entries as grouped transaction entries. The device may provide an indication that the grouped transaction entries and the set of matched transaction entries are reconciled transactions.