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

AI models significantly outperform traditional financial forecasting methods by capturing complex, nonlinear patterns in financial data that conventional approaches miss, leading to improved predictive accuracy across credit risk, asset pricing, and macroeconomic modeling.

Explainability tools are becoming essential for responsible AI in finance: The review highlights that methods like SHAP (SHapley Additive exPlanations) and feature importance analysis are increasingly used to make AI financial decisions transparent and interpretable, addressing concerns about "black box" decision-making in high-stakes financial contexts.

Data quality and generalizability remain critical unresolved challenges: Despite AI's promise, the review identifies that issues with data quality and the ability of models to perform reliably across different financial contexts and time periods continue to limit real-world deployment and trustworthiness.

Hybrid approaches combining AI with traditional statistical methods show promise: Rather than replacing conventional finance entirely, the most effective applications integrate machine learning and deep learning with established statistical techniques, suggesting a complementary rather than replacement relationship.

Responsible AI integration is an emerging priority in financial applications: The review notes that responsible AI practices are increasingly being incorporated into financial modeling, indicating growing recognition within the field that ethical considerations and safeguards must accompany AI innovation in this consequential sector.