Predictive Revenue Distribution via Machine Learning
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Solution Overview
Problem
Small and medium-sized businesses face delays in receiving settlement funding for completed transactions, which can disrupt their cash flow and hinder their ability to purchase inventory, as conventional computing processes for dispersing funding are inefficient.
Innovation Solution
A revenue distribution system that uses machine learning to predict revenue amounts for merchant accounts and provides advance funding through a Real-Time Payment (RTP) network, accounting for historical data, seasonal patterns, and other factors, and adjusts deposits based on actual transaction amounts to ensure timely and accurate funding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional computing processes are used for dispersing funding, then the system is simple to implement, but the funding delivery is delayed and inefficient
Solution Approach 1:
The system performs preliminary actions by using machine learning models to predict future revenue amounts and generate advance funding deposits before the actual transactions occur. The revenue distribution system analyzes historical data, seasonal patterns, and other factors to forecast revenue and deposit funds in advance, enabling merchants to access capital before their transactions are settled.
Solution Approach 2:
The patent replaces conventional mechanical computing processes with machine learning-based predictive analytics. Instead of using traditional batch processing systems, the invention employs AI models that continuously learn from merchant data to predict revenue patterns and automate advance funding decisions, substituting manual or rule-based systems with intelligent automated decision-making.
2Reliability
If advance funding is provided based on predictions, then cash flow disruption is reduced, but prediction accuracy may vary
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring actual transaction amounts against predicted revenue and adjusting future predictions accordingly. The machine learning models incorporate feedback loops that learn from discrepancies between predicted and actual performance, refining their accuracy over time. This feedback enables the system to adapt to changing merchant behaviors and improve prediction reliability.
Solution Approach 2:
The system performs preliminary analysis of historical data, seasonal patterns, and merchant-specific factors to generate advance funding predictions before transactions occur. By preparing and depositing funds in advance based on predictive models, the system ensures cash flow stability while managing the inherent uncertainties of prediction through continuous model refinement.
3Measurement precision
If multiple factors are considered in prediction, then prediction accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary processing by pre-calculating and storing merchant-specific parameters, historical patterns, and seasonal factors before actual prediction is needed. The machine learning models are pre-trained on extensive historical data, allowing them to make accurate predictions quickly without processing all factors in real-time during the prediction moment.
Solution Approach 2:
The patent segments the prediction process into distinct components, analyzing different factors (historical data, seasonal patterns, merchant-specific parameters) separately and combining their insights through the machine learning model. This segmentation allows the system to process complex multi-factor predictions efficiently by breaking down the computation into manageable, parallelizable tasks.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for providing predictive revenue distribution using machine learning and a Real-Time Payment (RTP) network. A revenue distribution system may train and apply a machine learning model to merchant transaction records to generate a predicted revenue amount. The revenue distribution system may deposit predicted revenue amounts to provide predictive funding for merchant accounts. For example, this may provide same-day and/or next-day funding. The revenue distribution system may account for differences in predicted revenue amounts and actual transaction amounts by adjusting the deposits of subsequent days. The revenue distribution system may also account for different periods of time, such as several days, a week, or longer. The revenue distribution system may also detect potentially fraudulent transaction amounts and/or may aid in monitoring business performance based on a detected difference between a predicted revenue amount and a provided transaction amount.