August 12, 2025

Harnessing AI and Machine Learning for Financial Forecasting: A Systematic Literature Review

Key Points

Models based on artificial intelligence and neural networks significantly improve financial forecasting accuracy, and hybrid models are particularly effective.
The review synthesized findings from 15 articles published between 2019 to 2025, demonstrating diverse applications of AI and machine learning in finance.
Assessment included big data analytics and sentiment analysis, showing their influence on the quality of accounting information systems.
Adoption of explainable AI is emphasized to promote transparency and foster accountable decision-making in financial contexts.

Abstract

The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies has significantly transformed the fields of finance and accounting, particularly in financial forecasting. This article presents a systematic literature review of 15 recent scientific publications from 2019 to 2025 that explore the application of AI and ML in predicting prices, earnings, cash flows, and the quality of accounting information. The findings show that models based on artificial neural networks, evolutionary optimization algorithms, and hybrid models such as DLSTM and GRU significantly improve financial prediction accuracy. Additionally, sentiment analysis and big data analytics (BDA) approaches have also proven to enhance the quality of accounting information systems. This review emphasizes the importance of AI adoption not only from a technical standpoint but also by considering transparency, multidisciplinary collaboration, and adaptation to local regulatory contexts. Recommendations are provided to encourage further research and the development of Explainable AI (XAI) models to support accountable data-driven financial decision-making.

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