Global Modeling Protection Architecture for Secure Exchange Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in securely managing sensitive data across multiple platforms, leading to increased fraud potential and resource inefficiencies due to dispersed data storage and manual analysis, which complicates data protection and exchange processes.
Innovation Solution
A centralized data protection system utilizing machine learning models to consolidate and analyze exchange patterns, detect anomalies, and optimize transactions, integrating a first ML model for trend mapping and a second ML model for generating reports and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored across multiple platforms, then data accessibility is improved, but data security deteriorates due to increased fraud potential
Solution Approach 1:
The patent introduces a centralized data protection system that acts as an intermediary between multiple data platforms. This system consolidates sensitive data into a single secure location while still allowing authorized access across platforms, thereby maintaining data accessibility without the security risks of dispersed storage.
Solution Approach 2:
The patent merges multiple data storage locations into a single centralized repository. By combining data that was previously scattered across multiple platforms into one secure location, the system maintains the functional benefits of multi-platform access while eliminating the security vulnerabilities of distributed storage.
2Reliability
If data is stored in a centralized location, then data security is improved, but system complexity increases
Solution Approach 1:
The centralized data protection system includes automated machine learning models that perform trend mapping, anomaly detection, and report generation without requiring manual intervention. This self-service capability reduces operational complexity while maintaining high security standards through automated, consistent enforcement of security protocols.
Solution Approach 2:
The centralized system performs multiple functions including data consolidation, trend analysis, anomaly detection, and automated reporting through a single multi-functional platform. This universal approach consolidates what would otherwise require multiple separate systems, reducing overall system complexity while enhancing security.
3Ease of operation
If manual analysis is used for data exchange verification, then operational simplicity is maintained, but productivity deteriorates due to resource inefficiencies
Solution Approach 1:
The patent replaces manual analysis processes with automated machine learning models that perform trend mapping, anomaly detection, and verification. This substitution eliminates the time-consuming and resource-intensive nature of manual analysis while maintaining ease of operation through automated, hands-off processing.
Solution Approach 2:
The system implements automated feedback loops where machine learning models continuously analyze data exchanges, detect anomalies, and generate reports. This automated feedback mechanism improves productivity by providing real-time monitoring and verification without requiring manual resources, while the systematic approach maintains operational simplicity.
Data Source
AI summary
Systems, methods, and computer-readable storage media for global modeling. One system includes a first data structure, a second data structure, a machine learning (ML) system and a processing circuit. The processing circuits includes one or more processors and memory storing instructions that, when executed, cause the processing circuit to determine trends corresponding to the one or more accounts for a third-party entity of the plurality of entities and transaction types. The instructions further cause the processing circuit to receive a request for a report. The instructions further cause the processing circuit to retrieve an exchange history. The instructions further cause the processing circuit to determine the corresponding data item. The instructions further cause the processing circuit to generate the report according to the request, the report including a content item including information corresponding to the trend map for the subset of third-party entities.