Personalized Tender Options for Faster Invoice Payments
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Solution Overview
Problem
Conventional invoice systems are time-consuming and inefficient, requiring sellers to wait for buyers to pay via cash, check, or credit card, often leading to delayed funds and increased operational challenges.
Innovation Solution
A network-based service provider system that intelligently generates and manages invoices, offering personalized and customized payment options for buyers, including loans and peer-to-peer transactions, and incentivizes timely payments through dynamic incentives based on transaction data and seller conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional invoice payment methods (cash, check, credit card) are used, then buyers can make payments, but sellers experience delayed funds availability and increased operational time consumption
Solution Approach 1:
The system performs preliminary actions by pre-establishing payment authorizations and incentives before the actual payment is due. Buyers can pre-authorize payment methods, and the system can pre-calculate and offer personalized incentives, so that when payment time arrives, the transaction is already streamlined and ready to execute immediately, eliminating delays.
Solution Approach 2:
The system implements continuous feedback loops where sellers receive real-time notifications about payment status, and buyers receive updates on their payment history and available incentives. This feedback mechanism keeps both parties informed and engaged, reducing the time sellers need to spend following up on payments and improving overall invoice management efficiency.
2Reliability
If sellers invest resources in generating invoices and following up with buyers for payment, then invoice management can be maintained, but operational resources and time are consumed
Solution Approach 1:
The system enables self-service by automatically generating invoices, tracking payment status, and managing follow-up communications without requiring seller intervention. The automated system handles routine invoice management tasks, ensuring reliable invoice tracking while minimizing the operational resources and time sellers need to invest.
Solution Approach 2:
The system provides universal invoice management capabilities that handle multiple functions including invoice generation, payment tracking, reminder scheduling, and incentive calculation within a single integrated platform. This multi-functionality consolidates various operational tasks into one system, reducing complexity while maintaining comprehensive invoice management reliability.
3Productivity
If buyers pay via traditional methods (cash, check, credit card), then payment can be processed, but sellers deny funds while waiting and face cash flow challenges
Solution Approach 1:
The system enables preliminary payment actions through pre-authorizations and advance payment options. Buyers can pre-commit to payment methods, and the system can process payments ahead of scheduled due dates when incentives are applied, allowing sellers to receive funds sooner and improve cash flow availability without losing the benefit of incentive-driven timely payments.
4Adaptability or versatility
If personalized and customized payment options are offered, then buyer convenience and seller cash flow are improved, but system complexity increases
Solution Approach 1:
The system implements dynamic payment options that automatically adapt to each buyer's profile, transaction history, and current financial状况. Payment terms, incentive structures, and available methods are dynamically adjusted based on real-time data, providing personalized customization without requiring manual configuration or increasing perceived system complexity for users.
Data Source
AI summary
Intelligent tender options for payment are described. In an example, buyer and seller profiles can be stored in a datastore associated with a service provider. The profiles can include interaction data including historical transaction data. A machine learning model can be trained using at least a portion of the historical transaction data. Responsive to receiving transaction data for a current transaction, a personalized plurality of tender options is determined based at least in part on (i) the machine learning model, (ii) one or more of a buyer profile, a seller profile, or the transaction data, and (iii) one or more services of the service provider available to the buyer based at least in part on the buyer profile. A GUI is presented on a seller or buyer device including a plurality of actuation mechanisms associated with respective tender options.