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Abstract
Governments worldwide face persistent challenges in managing cash flow effectively and ensuring timely payments to vendors, employees, and service providers. Traditional financial planning methods often fall short in predicting dynamic fiscal needs, leading to payment delays, budgetary inefficiencies, and reputational risks. This research explores the application of Artificial Intelligence (AI)-powered predictive analytics as a transformative approach to modernize government financial management. By leveraging machine learning models trained on historical financial data, budget patterns, and disbursement trends, the proposed framework enhances the accuracy of cash flow forecasting and anticipates payment bottlenecks before they occur. A case study of a national treasury department demonstrates the model's effectiveness in improving payment timeliness by over 20% and reducing end-of-quarter cash shortages by 15%. The study integrates supervised learning techniques, time-series forecasting, and anomaly detection to support decision-makers in anticipating fiscal risks and planning disbursements proactively. The results underscore AI’s potential to improve fiscal transparency, strengthen public trust, and align government disbursement practices with good financial governance. The paper also highlights practical implementation challenges—including data integration, model explainability, and policy alignment—and provides recommendations for governments aiming to adopt AI-driven tools in their financial workflows.
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