Machine Learning Payment Routing System for Account Selection
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Solution Overview
Problem
Existing payment systems lack flexibility in determining the most advantageous account to use for transactions, often resulting in unnecessary costs and requiring manual effort to balance funds across different accounts, as they are typically linked to a specific payment device without considering the customer's financial goals or health.
Innovation Solution
A transaction approval system that uses machine learning algorithms to intelligently route payment requests across multiple customer accounts based on financial goals and health, automatically selecting the most suitable account for transactions, including the option to advance funds or split payments, thereby optimizing costs and aligning with customer preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If funds are transferred from the account linked to the payment device, then the transaction is processed quickly, but transaction fees are incurred and account balance may not match customer intentions
Solution Approach 1:
The system performs preliminary analysis of customer accounts, balances, and financial goals before processing the transaction. The machine learning model pre-evaluates multiple accounts and their suitability for the transaction, enabling optimal account selection that minimizes fees while ensuring sufficient funds are available.
Solution Approach 2:
The patent introduces an intermediary system (the transaction approval system with machine learning model) that sits between the payment device and the account selection process. This intermediary analyzes transaction characteristics, customer preferences, and account states to intelligently route transactions to the most appropriate account, balancing speed and cost considerations.
2Ease of operation
If the customer manually transfers money between accounts to balance funds, then account preferences are maintained, but time and effort are required
Solution Approach 1:
The system enables self-service by automatically managing account selection and fund balancing without requiring customer intervention. The machine learning model autonomously determines the optimal account for each transaction based on customer-defined goals and preferences, eliminating the need for manual money transfers while maintaining account balance integrity.
Solution Approach 2:
The system implements feedback loops where transaction outcomes, account balances, and customer preferences are continuously monitored and fed back into the machine learning model. This allows the system to learn from past transactions and improve its account selection accuracy over time, automatically adapting to changing customer needs and account states.
3Productivity
If a specific account is linked to the payment device, then transaction approval is straightforward, but flexibility in account selection is limited
Solution Approach 1:
The patent transforms the static account-linking model into a dynamic system where account selection adapts in real-time based on transaction characteristics, customer goals, and account states. The machine learning model continuously evaluates multiple accounts and dynamically selects the most appropriate one for each transaction, maintaining high approval efficiency while providing versatile account selection options.
Solution Approach 2:
The transaction approval system serves multiple functions: it processes transactions, selects optimal accounts, manages fund balancing, and learns from customer behavior patterns. This multi-functional system handles diverse transaction types and customer preferences through a unified machine learning framework, providing both efficiency and adaptability.
Data Source
AI summary
A transaction approval system makes an assessment in real time whether to approve a purchase transaction and to select an account of the customer to fund the purchase transaction. The transaction approval system can use a machine learning model to select the appropriate account of the customer to fund a purchase transaction that satisfies one or more preferences or goals established by the customer or the system. The machine learning model can receive, as inputs, information on one or more of the customer's accounts, user preference information, information regarding the transaction and/or information regarding prior transactions by the customer and use the inputs and machine learning model to make a selection of a customer account that satisfies the preferences or goals established by the customer or system.