Machine Learning and Deep Learning in Computational Finance: A Systematic Review

TL;DR

This systematic review highlights how machine learning and deep learning have revolutionized financial forecasting and modeling, outperforming traditional methods and emphasizing transparency, with ongoing challenges in interpretability and data quality.

Contribution

It provides a comprehensive overview of recent AI applications in finance, identifying key models, trends, and future research directions based on 22 recent studies.

Findings

ML and DL outperform traditional models in accuracy

Explainable AI enhances transparency in financial models

Emerging cross-domain and responsible AI applications

Abstract

This systematic review examines how machine learning (ML) and deep learning (DL) have transformed forecasting, decision-making, and financial modelling, promoting innovation and efficiency in financial systems. Following PRISMA 2020 guidelines, we analyze 22 peer-reviewed and open-access articles (2024 to 2026) indexed in Scopus, applying ML and DL models across credit risk prediction, cryptocurrency, asset pricing, and macroeconomic policy modeling. The most used models include Random Forest, XG-Boost, Support Vector Machine, Long Short-Term Memory (LSTM), Bidirectional LSTM, Convolutional Neural Network (CNN), and hybrid or ensemble approaches combining statistical and AI methods. ML and DL techniques outperform traditional models by capturing nonlinear dependencies and enhancing predictive accuracy, while explainable AI methods (e.g., SHAP and feature importance analysis) improve transparency and interpretability. Emerging trends include cross-domain applications and the integration of responsible AI in finance. Despite notable progress, challenges remain in interpretability, generalizability, and data quality. Overall, this review provides a comprehensive overview of AI-driven computational finance and outlines future research directions.