Quantifying the Multidimensional Impact of Cyber Attacks in Digital Financial Services: A Systematic Literature Review

The increasing frequency and sophistication of cyber attacks have posed significant challenges for digital financial organisations, particularly in quantifying their multidimensional impacts. These challenges are largely attributed to the lack of a standardised cyber impact taxonomy, limited data availability, and the evolving nature of technological threats. As a result, organisations often struggle with ineffective security investment prioritisation, reactive incident response planning, and the inability to implement robust, risk-based controls. Hence, an efficient and comprehensive approach is needed to quantify the diverse impacts of cyber attacks in digital financial services. This paper presents a systematic review and examination of the state of the art in cyber impact quantification, with a particular focus on digital financial organisations. Based on a structured search strategy, 44 articles (out of 637) were selected for in-depth analysis. The review investigates the terminologies used to describe cyber impacts, categorises current quantification techniques (pre-attack and post-attack), and identifies the most commonly utilised internal and external data sources. Furthermore, it explores the application of Machine Learning (ML) and Deep Learning (DL) techniques in cyber security risk quantification. Our findings reveal a significant lack of standardised taxonomy for describing and quantifying the multidimensional impact of cyberattacks across physical, digital, economic, psychological, reputational, and societal dimensions. Lastly, open issues and future research directions are discussed. This work provides insights for researchers and professionals by consolidating and identifying quantification technique gaps in cyber security risk quantification.