AI-Assisted Decision Support Systems for Financial Managers

Abstract

The increasing complexity of financial management due to digital transactions, globalization, and rapidly growing financial data has exposed the limitations of traditional decision-making approaches. Artificial Intelligence (AI)-Assisted Decision Support Systems (AI-DSS) have emerged as effective solutions by integrating machine learning, deep learning, predictive analytics, and optimization techniques to enhance financial decision-making. This study proposes an intelligent AI-based financial decision support framework that integrates data from enterprise resource planning systems, banking transactions, accounting software, stock markets, customer relationship management systems, and external economic indicators. Advanced data preprocessing techniques, including data cleaning, feature engineering, normalization, and anomaly detection, improve data quality prior to model training. The framework employs supervised and ensemble learning models for financial forecasting, risk assessment, fraud detection, investment evaluation, and budget optimization, while reinforcement learning continuously refines decision strategies under dynamic market conditions. Explainable Artificial Intelligence (XAI) enhances transparency by providing interpretable recommendations that improve user trust and decision confidence. Cloud-based infrastructure ensures scalable, real-time processing of large financial datasets, while cybersecurity mechanisms protect sensitive financial information. Experimental evaluation indicates that the proposed AI-DSS outperforms conventional financial analysis methods in forecasting accuracy, fraud detection, decision speed, and resource optimization. Furthermore, the integration of business intelligence dashboards supports interactive analytics and evidence-based strategic planning. Overall, the proposed framework offers a scalable, secure, and explainable solution for intelligent financial management, enabling organizations to improve financial planning, investment decisions, risk mitigation, and long-term sustainability. Future research may explore the integration of generative AI, federated learning, and quantum-inspired optimization to further enhance next-generation financial decision support systems.