AI-Powered Financial Forecasting: Methodologies, Applications, and Case Studies

Abstract

The paper explored the disruptive nature of Artificial Intelligence (AI) and Machine Learning (ML) in the field of financial forecasting, comparing these new methods with the old statistical models. Furthermore, this study explored the effectiveness of deep learning models, i.e., Long Short-Term Memory (LSTM) networks and Recurrent Neural Networks (RNNs), with the traditional time-series models, i.e., ARIMA, and assessed the effectiveness of AI-based tools in predictive accuracy in stock market trends, credit risk assessment, and fraud detection through a systematic review and comparative analysis of recent literature. Also, this study examined the use of other data sources, such as Natural Language Processing (NLP) in sentiment analysis, and new paradigms, such as Quantum Computing in finance. Findings revealed that while AI models were more adaptable to non-linear market volatility, they posed serious issues in terms of data quality, computing expenses, and the issue of black box interpretability. Finally, this study found that hybrid models that integrate human experience with AI-based insights offered the strongest model in making future financial decisions.