Refund Model Predicting Processing Time
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Solution Overview
Problem
The variability in refund processing times for consumers is unclear, affecting financial planning and customer service, as merchants and consumers lack precise information on when refunds will be credited, leading to inefficiencies and resource wastage in manual investigative processes.
Innovation Solution
A refund management platform that uses historical data to train a refund model, predicting refund times based on merchant, shipping, and user account data, providing predictions with confidence measures and enabling automated actions such as notifications and financial planning recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual investigative processes are used to determine refund times, then consumers and merchants can obtain refund information, but the process is inefficient and wastes computing resources
Solution Approach 1:
The patent replaces manual investigative processes with an automated machine learning model that predicts refund times based on historical data. The refund management platform automatically receives return data, inputs it to the trained refund model, and generates predicted refund times without human intervention, thereby eliminating the inefficiencies of manual investigation.
2Loss of information
If refund processing times are variable and unpredictable, then merchants and consumers lack precise information for financial planning, but implementing prediction systems requires additional computational resources
Solution Approach 1:
The patent applies preliminary action by pre-training the refund model using historical refund data before deployment. The model is trained offline on past refund transactions, merchant data, shipping data, and user account data, so that when actual return data needs to be processed, the prediction can be made immediately without requiring complex real-time analysis. This prepares the system in advance to provide accurate predictions with minimal computational overhead during operation.
3Measurement precision
If accurate refund time predictions are provided, then financial planning and customer service are enhanced, but the model requires training on extensive historical data
Solution Approach 1:
The patent segments the refund prediction process into distinct components: the refund model that predicts processing time, merchant data that identifies merchant-specific patterns, shipping data that accounts for logistics variations, and user account data that captures individual consumer behaviors. By segmenting the historical refund data into these categories, the model can learn from diverse factors simultaneously, improving prediction accuracy while organizing the training process into manageable segments that can be processed efficiently.
Data Source
AI summary
A device may obtain historical refund data, the historical refund data including at least one of: merchant refund time data, shipping time data, or user account refund time data. The device may generate, based on the historical refund data, a refund model, the refund model being trained to receive, as input, at least one of: merchant data identifying a merchant, shipping data identifying a shipping entity, or account data identifying a user account entity. The device may receive return data indicating that a user account is to be credited, the return data including the merchant data. The device may determine a predicted refund time based on the return data and the refund model, and perform an action based on the predicted refund time.