ML-Driven Data Reconciliation for Automated Self-Healing
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Solution Overview
Problem
Current data integration and reconciliation technologies are inefficient and prone to errors, with manual reconciliation being labor-intensive and automated static processes often failing to catch all integration errors.
Innovation Solution
Implementing an automated data integration and reconciliation process using machine learning, where the number of reconciliation iterations is dynamically determined by a machine learning model, allowing for self-healing operations to correct errors and update the model based on reconciliation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data reconciliation is performed, then data consistency can be verified, but the process becomes labor-intensive and error-prone
Solution Approach 1:
The system performs automated data reconciliation without requiring manual intervention. The automated reconciliation process compares integrated data with source data, identifies discrepancies, and executes self-healing operations to correct errors, thereby eliminating manual labor while maintaining verification reliability
Solution Approach 2:
The patent replaces manual mechanical reconciliation processes with an automated computer-based system that uses machine learning algorithms to perform data comparison, error detection, and correction, substituting human operations with automated computational mechanisms
2Reliability
If a static automated reconciliation process is used, then accuracy improves compared to manual processes, but the process becomes inefficient and may miss certain integration errors
Solution Approach 1:
The system dynamically adjusts the number of reconciliation iterations based on machine learning predictions rather than using a fixed static number. The machine learning model analyzes historical data and predicts the optimal number of iterations needed to achieve consistent results, making the process adaptive and efficient
Solution Approach 2:
The system incorporates feedback loops where reconciliation results are used to train and update the machine learning model. The model continuously learns from past reconciliation outcomes to improve its predictions about the number of iterations needed, creating a self-improving system that optimizes both accuracy and efficiency over time
3Device complexity
If a fixed number of reconciliation iterations is performed, then the process is simple to implement, but unnecessary checks are performed reducing efficiency
Solution Approach 1:
The system changes the parameter of iteration count from a fixed static value to a dynamically determined value based on machine learning predictions. The machine learning model analyzes historical reconciliation data and predicts the optimal number of iterations needed, adjusting this parameter adaptively to eliminate unnecessary checks while maintaining sufficient verification
Data Source
AI summary
Technologies are described for performing automated data integration, reconciliation, and/or self-healing using machine learning. For example, data integration can be checked using a reconciliation procedure. The number of times that the reconciliation is performed can be determined dynamically by a machine learning model. For each iteration, reconciliation can be performed to check integrated data against source data. If any reconciliation errors are found, then self-healing operations can be performed. Results of the reconciliation can be output. The reconciliation results can be used to update the machine learning model so that the machine learning model can dynamically adjust the number of iterations to perform based at least in part on reconciliation results.