GUI Rendering Engine Front-Loads ML Fraud Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning processing delays during computation and classification can impact user experience and lead to incomplete determinations, especially in complex models with high dimensionality.
Innovation Solution
A machine learning approach combined with a specially configured graphical user interface rendering engine that front-loads machine learning processing by invoking it during intermediate user interface inputs, rather than at final confirmation, to obtain additional processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning validation is conducted at final transaction confirmation state, then classification accuracy can be improved, but user experience delay increases
Solution Approach 1:
The system performs machine learning classification computations in advance during intermediate user interface states rather than waiting until final confirmation. The rendering engine invokes machine learning processing during intermediate inputs, allowing the model to converge towards target classification earlier in the user journey, thus reducing delay at final confirmation while maintaining accuracy.
2Measurement precision
If complex machine learning models with high dimensionality are used, then classification accuracy is improved, but processing time increases
Solution Approach 1:
Complex machine learning models are invoked during intermediate user interface states to perform classification computations in advance. This preliminary action allows sufficient processing time for complex models to converge towards accurate classifications without impacting final user experience, as the computation occurs during the user's traversal through intermediate steps.
Solution Approach 2:
The system dynamically adjusts the timing and depth of machine learning processing based on user interaction pace. As users traverse through user interface screens at different speeds, the system opportunistically recruits additional machine learning instances for deeper checking against additional databases when more time is available, optimizing the balance between model complexity and processing time.
3Measurement precision
If additional verification levels are conducted, then fraud detection accuracy is improved, but processing time increases
Solution Approach 1:
The system dynamically adjusts verification depth based on available time during user traversal. Different machine learning instances can be recruited for additional or deeper checking against additional databases such as local instances for different jurisdictions, depending on how much time the user spends in intermediate states. This opportunistic approach allows deeper verification when time permits without mandating it for all users.
Data Source
AI summary
A specific machine learning approach in combination with a specially configured graphical user interface rendering engine is proposed wherein the rendering of a graphical user interface is operated in concert with machine learning to provide additional processing time for machine learning during intermediate user interface process flows. By front-loading the analysis computing process, the additional processing time whereby the user traverses a sequence of graphical user interface pages is utilized for background processing of the primary identifier using a subroutine running one or more trained machine learning models that are refining an fraud estimation score during the additional processing time. Variations are also proposed where different variations of opportunistic machine learning processing chains are inserted into a processing chain if additional processing time is available after processing using a main machine learning processing model is completed.