ML-Based Database Inconsistency Detection System
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Solution Overview
Problem
Banks face challenges in managing inconsistencies across their shared databases, which can lead to redundant data, unnecessary database generation, and inefficient data management.
Innovation Solution
A system and method utilizing a machine learning model to identify inconsistencies in data across shared databases by collecting data from multiple databases, processing it through the model, and transmitting results to facilitate remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in multiple independent databases across different divisions, then data accessibility and divisional autonomy are improved, but data inconsistencies and redundancy increase
Solution Approach 1:
The system implements automated feedback mechanisms by continuously monitoring data across databases and using machine learning models to detect inconsistencies. When inconsistencies are detected, the system generates notifications and alerts relevant personnel, creating a closed-loop feedback system that maintains data consistency without restricting divisional autonomy.
Solution Approach 2:
The patent introduces an intermediary layer consisting of the backend server and machine learning model that mediates between multiple independent databases. This intermediary automatically detects and reports inconsistencies without requiring changes to the underlying database structure or access protocols, thus preserving divisional autonomy while improving data consistency.
2Measurement precision
If manual monitoring of database inconsistencies is performed, then detection accuracy can be maintained, but processing time and operational costs increase
Solution Approach 1:
The system enables self-service automated monitoring where the machine learning model independently collects data from multiple databases, processes information, detects inconsistencies, and generates notifications without human intervention. This eliminates manual monitoring while maintaining high detection accuracy and significantly reducing processing time.
Solution Approach 2:
The patent replaces manual mechanical monitoring processes with an automated electronic system using machine learning algorithms. The ML model automatically analyzes data patterns, detects inconsistencies, and generates reports, substituting human operators with an efficient automated system that processes data faster and with consistent accuracy.
3Productivity
If traditional inconsistency detection methods are used, then implementation simplicity is maintained, but detection efficiency and accuracy decrease
Solution Approach 1:
The machine learning model serves multiple functions: collecting data from diverse database sources, processing and analyzing information, detecting various types of inconsistencies, and generating notifications. This multi-functional approach improves detection efficiency without requiring separate specialized systems for each function, thus managing complexity effectively.
Data Source
AI summary
A system for determining whether inconsistencies exist in an entity's shared databases using a machine learning model. The system includes a repository having a plurality of databases that store data and information in a format accessible to users, and a back-end server operatively coupled to the repository and being responsive to the data and information from all of the databases. The back-end server includes a processor for processing the data and information, a communications interface communicatively coupled to the processor, and a memory device storing data and executable code. The code causes the processor to collect data and information from the databases, store the collected data and information in the memory device, process the stored data and information through the machine learning model to determine whether inconsistencies in the data exist in the databases, and transmit a communication on the interface identifying whether inconsistencies do exist in the databases.