The Role of Decision Support Systems in Automated Financial Advising: A Review of Computational Models

Abstract

Decision Support Systems (DSS) have emerged as intelligent tools in automated financial advising, aiming to enhance decision-making processes and improve accuracy and efficiency. With the advent of new technologies such as artificial intelligence and machine learning, these systems have contributed to reducing costs and improving the quality of financial advice. The objective of this article is to examine computational models in Decision Support Systems and analyze their impact on automated financial advising. This study was conducted as a review and analysis of reliable sources in the field of DSS. The computational models analyzed include mathematical and statistical models, artificial intelligence, machine learning, neural networks, and hybrid models. Real-world examples of DSS platforms in the financial advising market were also reviewed, evaluating their successes and limitations. Findings indicate that DSS have significantly enhanced the accuracy and efficiency of financial decision-making, reducing costs while offering real-time advice. However, challenges such as data quality, system complexity, and reliance on specific algorithms persist, requiring future improvements and development. Decision Support Systems play a critical role in improving automated financial advising, but further advancements in data quality, algorithm development, and enhancing human interaction are essential for greater efficiency. The future of DSS lies in integrating financial and non-financial data and advancing hybrid models.