Machine Learning Front-End Device Selection for Limits and Travel
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Front-end devices such as ATMs and PoS terminals often have action limits (e.g., withdrawal maxima, supply levels, and hour restrictions) that can lead to wasted power and processing resources when users attempt actions that violate these limits, and selecting the closest device may not be resource-efficient due to unnecessary travel.
Innovation Solution
A machine learning model that considers withdrawal maxima, supply levels, and traffic information to select a relevant front-end device based on user requests, conserving power and processing resources by identifying devices capable of fulfilling the user's needs while minimizing travel time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users select the closest front-end device, then travel time is minimized, but power and processing resources are wasted when the device cannot fulfill the transaction due to limits
Solution Approach 1:
The system performs preliminary actions by checking device limits (cash availability, transaction maxima, hour restrictions) before directing users to a front-end device. The machine learning model predicts which devices can fulfill user requests by evaluating current device states, preventing wasted trips and resource consumption at incapable devices.
2Reliability
If front-end devices operate with fixed limits, then device control and security are maintained, but resource efficiency decreases when users are directed to devices that cannot fulfill requests
Solution Approach 1:
The system implements feedback by continuously monitoring device limits and states, then using this information to guide users to appropriate devices. The machine learning model receives feedback about device capabilities and adjusts routing recommendations accordingly, improving overall system efficiency while maintaining individual device control through their fixed limits.
Data Source
AI summary
In some implementations, a user device may transmit, to a routing system, a request that indicates an amount. The user device may additionally transmit, to the routing system, a current location associated with the user device and account information associated with a user of the user device. The user device may receive, from the routing system, an indication of at least one relevant front-end device, based on the amount, a time associated with the request, the current location, and the account information. The user device may output a representation of the at least one relevant front-end device.