Machine Learning Model for Payment Network Cost Estimation
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Solution Overview
Problem
Existing payment processing systems face computational inefficiencies and inaccuracies in managing complex network cost rules, which are often hard-coded and require manual updates, leading to increased resource utilization and potential errors due to lack of real-time updates from card networks.
Innovation Solution
A machine learning model is employed to automate the estimation of network costs, adapting to changes in cost rules by predicting fee descriptions and rates using decision trees and linear regression, reducing computational resource usage and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hard-coded cost rules are used in payment processing systems, then implementation simplicity is maintained, but adaptability to rule changes deteriorates and manual updates are required
Solution Approach 1:
The machine learning model automatically updates cost estimates by learning from actual cost data received from card networks, eliminating the need for manual rule updates. The system self-adjusts to rule changes by training on new data, making the payment processing system adaptive without requiring manual intervention or complex rule management infrastructure.
2Productivity
If manual updates of cost rules are performed, then resource utilization is reduced, but productivity and timeliness of adaptation deteriorate
Solution Approach 1:
The machine learning model performs preliminary learning during training phases using historical cost data, so that when rule changes occur, the system is already prepared to adapt quickly. The model pre-processes and learns cost patterns in advance, enabling rapid adaptation to new rules without requiring intensive real-time computational resources during transaction processing.
Solution Approach 2:
The patent replaces manual mechanical updates of cost rules with an automated machine learning system. Instead of manually updating rule sets, the ML model automatically learns and adapts to cost changes by processing actual cost data from card networks, significantly improving adaptation timeliness while optimizing computational resource usage through intelligent algorithms.
3Reliability
If complex cost rules are manually managed, then accuracy may be maintained through careful updates, but reliability deteriorates due to potential human errors
Solution Approach 1:
The machine learning model implements a feedback mechanism by continuously receiving actual cost data from card networks and using this information to refine and improve cost estimation accuracy. The model learns from the difference between predicted and actual costs, automatically adjusting its parameters to minimize errors. This closed-loop feedback system eliminates human error while maintaining high reliability and progressively improving accuracy over time.
4Productivity
If computational resources are increased to handle rule updates, then productivity improves, but loss of energy increases
Solution Approach 1:
The machine learning model applies partial action by focusing computational resources only on the specific cost estimation task rather than processing all possible transaction data. The model learns from representative samples of cost data and generalizes to new situations, achieving high processing efficiency without requiring excessive computational resources. This selective approach to learning optimizes the balance between productivity and energy consumption.
Data Source
AI summary
The subject technology generates training data based at least in part on reporting data from a card network. The subject technology trains a machine learning model based on the training data. The subject technology determines, using the machine learning model, a fee description for a transaction. The subject technology determines a variable rate and a fixed amount based at least in part on the fee description. The subject technology determines at least one cost based at least in part the variable rate and the fixed amount for each transaction from a set of transactions. The subject technology evaluates at least one result against second reporting data. The subject technology provides, for display, the at least one cost and the at least one result on a display of a client device.