AI Benchmarking System for Predictive Modeling via Data Merging
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Solution Overview
Problem
In a distributed network, secondary entities lack the ability to communicate or access data from each other, hindering comparative analysis and predictive modeling due to limited historical data access.
Innovation Solution
A system that collects and processes historical data from multiple secondary entity systems using a machine learning engine to predict future behaviors and provide real-time benchmarking insights, enabling comparative analysis and predictive modeling across entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If secondary entities operate independently without data sharing, then each entity maintains data security and system simplicity, but the ability to perform comparative analysis and predictive modeling deteriorates due to limited historical data access
Solution Approach 1:
The patent introduces a managing entity as an intermediary between secondary entities. This intermediary collects historical data from multiple secondary entities, processes it through machine learning models, and provides benchmarking insights back to individual entities. This resolves the contradiction by enabling data sharing and predictive modeling capabilities without requiring direct communication infrastructure between secondary entities, thus maintaining system simplicity while improving information access.
2Productivity
If secondary entities share data through a centralized system, then comparative analysis and predictive modeling improve, but the complexity of the system increases due to additional infrastructure requirements
Solution Approach 1:
The managing entity automatically collects data from secondary entities, trains machine learning models, generates benchmarking reports, and delivers insights back to entities without requiring manual intervention or complex coordination infrastructure. The system serves itself by autonomously performing data processing and model training, thus improving predictive modeling capability while minimizing the complexity of data sharing infrastructure.
3Measurement precision
If each secondary entity maintains its own historical data separately, then data security and system simplicity are maintained, but the quality and accuracy of predictive models deteriorate due to insufficient training data
Solution Approach 1:
The patent merges historical data from multiple secondary entities into a centralized dataset that is used to train machine learning models. By combining data across entities, the system achieves sufficient training data volume and diversity to produce accurate predictive models and meaningful benchmarking insights, while the managing entity handles the merging process automatically to minimize system complexity.
Data Source
AI summary
Embodiments of the invention are directed to systems, methods, and computer program products for utilizing machine learning to provide real-time benchmarking of an entity account. As such, the system allows for use of a machine learning engine to collect information from a plurality of sources and predict future account behavior associated with said sources. A single third party entity may lack enough historical data for accurate predictive modeling. By collecting data associated with a plurality of third party entities, the system may more accurately identify data trends and generate predictions of future account behavior. Thus, the system may benefit a number of entities, by providing real-time data analysis that would not be obtainable by any one entity operating alone.