Machine Learning Model for Identifying Confidential Information in Shared Databases
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Solution Overview
Problem
Banks face challenges in efficiently managing and identifying contractually required confidential information across their shared databases, which can lead to data redundancy, unauthorized sharing, and compliance issues.
Innovation Solution
A system and method utilizing a machine learning model to determine whether shared databases contain contractually required confidential information, involving a repository with multiple databases, a back-end server with processing capabilities, and communication interfaces to identify and remediate the presence of such information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If banks use machine learning models to automatically identify confidential information, then productivity and measurement precision are improved, but device complexity increases
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically scan, detect, and identify confidential information in databases without requiring manual intervention from bank personnel. The model autonomously processes data, applies classification algorithms, and generates reports, freeing employees from repetitive manual tasks while maintaining high accuracy in identification.
Solution Approach 2:
The patent replaces manual mechanical processes of data review and confidential information identification with an automated machine learning system. The ML model uses computational algorithms to analyze database contents, classify information based on contractual requirements, and flag sensitive data, substituting human expertise with an automated intelligent system that operates continuously without fatigue.
2Measurement precision
If banks implement comprehensive data collection from all databases, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing database structures before actual confidential information identification occurs. The machine learning model is trained in advance on sample data to recognize patterns and contractual requirements, enabling it to quickly and accurately identify confidential information in new databases without requiring time-consuming manual analysis during operation.
Solution Approach 2:
The patent implements continuous automated scanning of databases where the machine learning model operates continuously to identify and flag confidential information as it is accessed or updated. This continuous operation eliminates gaps in monitoring, ensures comprehensive coverage without repeated manual reviews, and maintains consistent accuracy while reducing overall time investment through parallel processing of multiple databases.
Data Source
AI summary
A system for determining whether an entity's shared databases include contractually required confidential information 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 the databases do include confidential information, and transmit a communication on the interface identifying whether the databases do include confidential information.