Geographic Agnostic Machine Learning Model for Payment Data
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Solution Overview
Problem
Machine learning models developed for specific geographic areas are not applicable to other areas, leading to the creation of numerous models and large amounts of data, which is inefficient and lacks scalability.
Innovation Solution
A geographic agnostic machine learning model is developed by selecting and formatting transaction data from multiple geographic areas, generating a generalization layer based on macro-economic factors, and applying inputs to this model for consistent performance across different regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are developed for each specific geographic area and demographic, then prediction accuracy for that area is improved, but the number of models and data requirements increase significantly
Solution Approach 1:
The patent applies universality by creating a single machine learning model architecture that can function across multiple geographic areas and demographics. The model uses standardized feature engineering and processing pipelines that work universally, eliminating the need to develop separate models for each region while maintaining predictive capabilities through adaptive parameter tuning and ensemble methods
2Measurement precision
If machine learning models are developed for each specific geographic area, then local prediction accuracy is improved, but data storage and processing requirements increase
Solution Approach 1:
The patent merges data from multiple geographic areas into a unified dataset for training a single model. By combining previously separate data silos into one comprehensive dataset with standardized schemas, the system reduces redundant data storage while improving model generalization across regions through shared patterns learned from diverse populations
3Reliability
If multiple machine learning models are created for different geographic areas, then local performance is optimized, but system maintenance and updates become more difficult
Solution Approach 1:
The patent segments the system into modular components including standardized data processing pipelines, feature extraction modules, and model training frameworks that can be independently maintained and updated. This segmentation allows local performance optimization through targeted module adjustments without requiring changes to the entire system, simplifying maintenance while preserving regional effectiveness
Data Source
AI summary
Provided is a system for developing a geographic agnostic machine learning model. The system may select transaction data associated with payment transactions conducted by a first plurality of users, wherein the transaction data includes first transaction data associated with payment transactions conducted by a first plurality of users in a first geographic area and second transaction data associated with payment transactions conducted by a second plurality of users in a second geographic area, normalize the first transaction data associated with payment transactions conducted by the first plurality of users in the first geographic area and the second transaction data associated with payment transactions conducted by the second plurality of users in the second geographic area to provide training data, generate a machine learning model using the training data, and determine a classification of an input using the machine learning model. A method and computer program product are also disclosed.