ML Budget Predictor for Deficit Forecasting
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Solution Overview
Problem
Conventional funds management systems face challenges in predicting and managing budget deficits and surpluses, leading to project disruptions, complex audits, and undesirable carry-forward processes, which can result in reduced budgets for subsequent periods.
Innovation Solution
A machine-learning-based budget utilization predictor system that uses historical funds management data to forecast whether a fund, funds center, or funded program will experience a budget deficit or surplus by the end of an accounting period, allowing for proactive measures to avoid deficits and optimize budget spending.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional funds management systems are used to track and compare budget consumable amount against consumed amount, then budget status can be monitored, but budget deficits cannot be predicted in advance leading to project disruptions and blocked purchase orders
Solution Approach 1:
The patent applies preliminary action by training a machine learning model on historical funds management data to predict future budget deficits before they occur. The system analyzes patterns from previous accounting periods and generates early warnings, enabling organizations to take preventive measures such as adjusting spending or securing additional funding before purchase orders are blocked and projects are disrupted.
2Quantity of substance
If budget surplus occurs at the end of accounting period, then available funds are preserved, but complex and time-consuming carry-forward processes are required to transfer surplus to following period
Solution Approach 1:
The patent applies preliminary action by predicting budget surplus conditions before the accounting period ends. The machine learning model analyzes historical data to forecast whether a surplus will occur, allowing organizations to proactively adjust spending patterns or plan for the surplus allocation in advance, thereby avoiding the need for complex end-of-period carry-forward processes.
3Reliability
If extensive auditing is conducted to rectify budget deficit situation, then budget compliance is ensured, but large amount of data processing and time are required
Solution Approach 1:
The patent applies preliminary action by using a machine learning model to predict budget deficits before they occur. By analyzing historical funds management data and identifying patterns that lead to deficits, the system provides early warnings that allow organizations to take corrective spending actions proactively, thereby avoiding the need for extensive post-deficit auditing and data processing.
Data Source
AI summary
A computer-implemented, machine-learning-based budget utilization predictor is disclosed herein. An embodiment of the predictor operates by receiving first funds management data associated with an entity, the first funds management data corresponding to a current accounting period; generating input data for a machine learning (ML) model from the first funds management data, the ML model being trained to predict whether a budget surplus or budget deficit will exist for the entity at the end of the current accounting period based on training data generated from second funds management data associated with the entity, the second funds management data corresponding to one or more previous accounting periods; providing the input data as input to the ML model; and obtaining as an output from the ML model a prediction of whether the budget surplus or budget deficit will exist for the entity at the end of the current accounting period.