Towards a better understanding of financial and economic systems’ complexities: some new evidence coming from artificial intelligence, machine learning and big data advanced technologies

The integration of Artificial Intelligence (AI) within financial institutions has accelerated in response to mounting complexities and recurrent global crises, revealing the well-known but fair limitations of traditional economic models. Enhanced computational capacity and Big Data (BD) availability have enabled the adoption of Machine Learning (ML) across fields from banking, asset and risk management, and insurance, for tasks such as credit scoring, fraud detection, and market surveillance. AI facilitates portfolio construction and risk budgeting by synthesising historical and alternative data, while Natural Language Processing (NLP) aids in interpreting regulatory texts and central bank communications. However, the opacity of AI models introduces novel risks, including among others validation challenges and systemic vulnerabilities due to model convergence. When controlled appropriately, AI can paradoxically serve both as a risk source and a mitigation tool. This special issue presents rigorously peer-reviewed contributions that explore AI, ML, and BD applications in asset pricing, risk management, macroeconomic forecasting, and sustainable finance, offering valuable insights for academics, practitioners, and policymakers navigating financial uncertainty.