Automated Cash Flow Forecasting for SMBs
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Solution Overview
Problem
Small and medium-sized businesses (SMBs) face challenges in managing cash flow due to the complexity of business-to-business payment transactions, leading to mismanagement and a high failure rate, as existing systems lack the efficiency to provide near real-time cash flow forecasting based on relevant data.
Innovation Solution
A computer-implemented method and system for automated cash flow forecasting that monitors payable and receivable transactions in a payment network, determines seasonal and non-seasonal variables, and generates forecasts using models like exponential smoothing and autoregressive integrated moving average, incorporating firmographics data for accurate near real-time predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual cash flow management methods are used, then businesses can track transactions, but the process is inefficient and lacks near real-time forecasting capability
Solution Approach 1:
The patent replaces manual cash flow management mechanisms with an automated computer-implemented system that monitors transactions and generates forecasts algorithmically. The system substitutes human analysis with machine learning models and automated processing, eliminating manual delays and enabling near real-time forecasting of cash flow based on monitored payable and receivable transactions.
Solution Approach 2:
The system enables businesses to automatically monitor their own transaction data and generate cash flow forecasts without external intervention. The automated system continuously tracks payable and receivable transactions, identifies seasonal variables, and produces forecasts independently, allowing businesses to self-serve their cash flow management needs in near real-time.
2Measurement precision
If existing forecasting systems are used, then some predictions can be made, but they lack accuracy due to inability to process complex B2B transaction data in near real-time
Solution Approach 1:
The patent segments the complex B2B transaction data into distinct components including payable transactions, receivable transactions, and seasonal variables. This segmentation allows the system to process each component separately and efficiently, improving both the speed of data processing and the accuracy of cash flow forecasts by analyzing specific transaction types and patterns independently.
Solution Approach 2:
The system dynamically changes and adjusts forecasting parameters based on monitored transaction data, identifying seasonal variables and adapting models to reflect current business conditions. This parameter adaptation enables the system to maintain high forecast accuracy while processing data efficiently, as the models evolve to match actual transaction patterns without requiring complete reprocessing of historical data.
3Reliability
If comprehensive transaction monitoring is implemented, then forecast accuracy improves, but system complexity increases
Solution Approach 1:
The patent creates a universal forecasting system that handles multiple transaction types (payable and receivable), identifies seasonal variables, and generates cash flow predictions through a single integrated platform. This multi-functional system consolidates what would otherwise require separate tools and processes, improving reliability through comprehensive monitoring while managing complexity through unified architecture that performs multiple functions cohesively.
Data Source
AI summary
A computer-implemented method for automated forecasting of cash flow includes: monitoring, while a plurality of first transactions are being processed in a payment network, payable transaction data associated with the plurality of first transactions, the plurality of first transactions initiated with at least one account issued to a merchant; monitoring, while a plurality of second transactions are being processed in a payment network, receivable transaction data associated with the plurality of second transactions, the plurality of second transactions between the merchant and a plurality of users; determining, based on the payable transaction data and the receivable transaction data, a plurality of seasonal variables; and generating a cash flow forecast associated with the merchant, the cash flow forecast generated based on the plurality of seasonal variables. A system and computer program product for automated forecasting of cash flow are also disclosed.