The Evolution of Machine Learning in Banking: A Bibliometric Mapping and Systematic Literature Review

Abstract

The rapid adoption of artificial intelligence (AI) and machine learning (ML) has transformed the banking sector, particularly in risk management, predictive analytics, fraud detection, customer behavior analysis, and financial decision-making. However, research on ML in banking remains fragmented across methodological and application domains, while emerging issues such as cybersecurity, privacy, explainability, ethical AI, and financial resilience receive comparatively less attention. This study aims to systematically map and analyze the evolution of ML research in banking by identifying dominant methodologies, research themes, conceptual relationships, temporal developments, and emerging research gaps. The study employs a Systematic Literature Review (SLR), bibliometric mapping, and content analysis based on 53 curated scientific articles. Literature selection follows the PRISMA framework, while bibliometric analysis uses VOSviewer through network, overlay, and density visualizations. The findings identify seven major research clusters: integrated banking predictive analytics; supervised ML for credit risk, fraud, and customer churn; deep learning and hybrid ML–DL; ensemble learning and imbalanced data handling; unsupervised learning and customer segmentation; anomaly detection and cybersecurity; and privacy-preserving, federated learning, and ethical AI. The results indicate that ML remains the dominant research theme, with deep learning, artificial neural networks, risk assessment, accuracy, and efficiency representing highly developed areas. Temporal analysis demonstrates a shift from conventional predictive methods toward deep learning, cybersecurity, privacy, blockchain, and emerging AI technologies. Nevertheless, predictive performance remains dominant, while explainability, privacy, fairness, governance, and financial stability remain comparatively underdeveloped. The study concludes that future banking ML research should move beyond algorithmic accuracy toward integrated frameworks combining predictive analytics with responsible AI, cybersecurity, privacy protection, financial stability, and institutional governance.

Keywords: Risk Management, Financial Technology, Cybersecurity, Ethical AI

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