AI in Finance: A Comparative Investigation of Machine Learning and Deep Learning Techniques for Financial Applications
DOI:
https://doi.org/10.54097/v10fbm91Keywords:
Artificial intelligence, finance, machine learning, deep learning.Abstract
The financial industry faces challenges like market volatility, large data volumes, and complex fraud, where traditional models like Autoregressive Integrated Moving Average (ARIMA) and logistic regression fall short. This paper reviews Artificial Intelligence (AI) applications in finance to overcome these limitations. It explores three domains: stock price forecasting, credit scoring, and fraud detection, employing AI techniques such as machine learning, deep learning, and hybrid models. These methods are compared with traditional approaches using recent studies, integrating sentiment analysis (e.g., FinBERT) and explainability tools e.g., SHapley Additive exPlanations (SHAP). Results show AI outperforms traditional methods, with up to 57% accuracy in stock prediction, 0.92 Area Under Curve (AUC) in credit risk, and 10% F1-score improvement in fraud detection. Challenges include interpretability, generalizability, privacy, and regulation. Future directions involve explainable AI, transfer learning, federated learning, and the EU AI Act. This review offers a comprehensive overview, guiding researchers and practitioners in AI-driven finance.
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