Data mining tools for cyber risk assessment in financial services

Abstract

Purpose. The purpose of this article is to analyse current approaches to cyber risk assessment in the financial sector and to explore the applicability of modern data analysis tools for enhancing the effectiveness of cybersecurity in financial institutions. Methods. The study is based on a comprehensive review of international regulatory frameworks (including the NIST Cybersecurity Framework, Basel Committee principles, DORA), analysis of quantitative and qualitative cyber risk metrics (e.g., incident frequency, financial losses, VaR, ES), and a structured classification of cyber threats according to ENISA. A comparative approach is employed to identify preventive and adaptive technologies used in cyber risk management. Results. The research identifies artificial intelligence, machine learning, and behavioural analytics as key technologies for cyber threat detection, incident prediction, and real-time response. The study outlines a detailed taxonomy of cyber threats – ransomware, malware, social engineering, insider threats, DDoS, and disinformation – and maps each to corresponding assessment tools, such as SIEM systems, EDR, IDPS, threat intelligence platforms, penetration testing, and vulnerability scanning. Additionally, compliance assessment tools supporting adherence to GDPR, ISO/IEC 27001, and PCI DSS standards are examined. The role of fraud detection, Darknet monitoring, and AI-based access protection is emphasized, along with predictive analytics as a means of anomaly detection and behavioural verification. Conclusions. The findings demonstrate that a multi-layered cybersecurity strategy that integrates real-time monitoring, advanced data analytics, and regulatory compliance tools is essential for managing cyber risks in the financial sector. The adoption of intelligent cyber risk assessment instruments significantly enhances the digital resilience and operational continuity of financial institutions in the context of growing cyber threats.