Abstract : Background: The rapid proliferation of artificial intelligence (AI) technologies has fundamentally transformed financial risk management practices globally. Emerging markets face unique challenges including data scarcity, regulatory fragmentation, institutional volatility, and heightened systemic risks. While AI offers unprecedented opportunities for enhanced risk assessment, fraud detection, and decision support, its application in emerging market contexts remains underexplored and fragmented across disciplinary boundaries. Objectives: This scoping review systematically maps the landscape of AI-driven financial risk management and decision intelligence in emerging markets. Specifically, it aims to: (1) identify the range and nature of AI techniques applied to financial risk domains; (2) synthesize evidence on their effectiveness, implementation challenges, and contextual adaptations; (3) examine the integration of decision intelligence frameworks; (4) highlight research gaps and future directions for theory, practice, and policy. Methods: Following the Arksey and O'Malley framework enhanced by Levac et al., we conducted a comprehensive literature search across four major databases (SciSpace, Google Scholar, ArXiv) covering publications from 2019 to 2025. After deduplication and relevance-based screening, 64 unique studies were retained and analyzed. Data extraction focused on AI methodologies, application domains, geographic contexts, performance outcomes, and decision support mechanisms. Thematic synthesis was employed to identify major patterns and knowledge clusters. Results: Five major themes emerged: (1) Machine Learning for Credit Risk Assessment and Financial Inclusion; (2) Deep Learning and Neural Networks for Market Prediction and Volatility Forecasting; (3) Natural Language Processing and Sentiment Analysis for Decision Support; (4) AI-Based Fraud Detection and Operational Risk Management; and (5) Explainable AI, Regulatory Technology, and Governance Frameworks. The review reveals significant heterogeneity in methodological rigor, limited empirical validation in emerging market settings, and nascent integration of decision intelligence principles. Performance improvements range from 15% to 35% over traditional methods, with neural networks and ensemble methods demonstrating superior predictive accuracy. Conclusion: AI-driven approaches show substantial promise for enhancing financial risk management in emerging markets, particularly in credit scoring, fraud detection, and market forecasting. However, critical gaps persist in explainability, regulatory alignment, ethical governance, and context-specific validation. Future research must prioritize hybrid human-AI decision frameworks, robust evaluation in diverse emerging market contexts, and development of regulatory technology solutions that balance innovation with systemic stability.
Iyer et al. (Mon,) studied this question.