Time-Aware Auto-Reconciliation for Dataset Discrepancies
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Solution Overview
Problem
Organizations face challenges in identifying and reconciling discrepancies among different datasets recording the same events due to varying reliability and inconsistencies in data entry or automated calculations, which hinders accurate data verification and resource allocation.
Innovation Solution
A time-aware machine learning model is employed to generate recommendations for auto-reconciling dataset discrepancies by analyzing event records, including attributes like time and magnitude, and determining whether to perform auto-reconciliation based on trained rules and thresholds, without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and reconciliation of dataset discrepancies is performed, then data accuracy can be verified, but the process becomes inefficient and cannot scale to vast amounts of data stored in different locations
Solution Approach 1:
The system enables datasets to self-reconcile by automatically generating and applying remediation records. The machine learning model analyzes discrepancies between datasets and autonomously determines how to reconcile them without human intervention, allowing the system to service itself at scale while maintaining accuracy.
Solution Approach 2:
The patent replaces manual mechanical processes of data verification with an automated machine learning system. The ML model substitutes human analysts by automatically identifying discrepancies, generating remediation records, and reconciling datasets, thereby scaling the reconciliation process to handle vast amounts of data across multiple locations and entities.
2Productivity
If auto-reconciliation is performed without human intervention, then productivity increases, but the reliability of reconciliation may deteriorate due to errors in automated processes
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously learns from reconciliation outcomes. By analyzing the results of auto-reconciliation operations and comparing them against ground truth data when available, the model refines its ability to generate accurate remediation records, improving reliability with each iteration while maintaining high productivity.
Solution Approach 2:
The system performs preliminary analysis and validation before finalizing reconciliations. The ML model pre-processes discrepancy data, identifies potential issues, and generates candidate remediation records that are then validated against multiple criteria before being applied, ensuring reliability is maintained even as productivity scales.
3Measurement precision
If comprehensive verification of all datasets is performed, then data accuracy is ensured, but the time and resources required increase significantly
Solution Approach 1:
The system extracts only the critical discrepancies between datasets rather than performing comprehensive verification of all data points. The machine learning model identifies and focuses on specific differences that need reconciliation, eliminating the need to manually review every record while still ensuring data accuracy through targeted analysis.
Data Source
AI summary
Techniques for remediating discrepancies between datasets by applying a trained, time-aware machine learning model to determine whether or not to auto-reconcile discrepancies are disclosed. To train a time-aware machine learning model, a system generates a training dataset of event records that records event attributes, including a time associated with the event and a magnitude associated with the event. The dataset of event records includes a first set of event records that are candidates for reconciliation and a second set of event records against which the first set would be reconciled. The time-aware machine learning model generates a recommendation for auto-reconciliation of dataset discrepancies based on discrepancy data and auto-reconciliation data. The discrepancy data and auto-reconciliation data are based on (a) a current time period or a time period corresponding to a current candidate remediation record and (b) time periods preceding the current time period.