Quantifying the Financial Impact of Cyber Security Attacks on Banks: A Big Data Analytics Approach

The banking industry is a frequent target of security attacks, and DDoS attacks are among the most common types that can cause significant financial losses. In this paper, we present a big data analytics approach to analyze 33.4 billion transactions of a sample bank over five years, identifying transaction types, acquiring terminals, and expected income. We estimate the demand load pattern during DDoS attacks' downtime and lost opportunities using pattern recognition. Our findings show that a DDoS attack can cost several thousand dollars per hour of downtime, which varies across different days and times. Our study contributes to the literature on the financial impact of security attacks on banks and has implications for developing more effective security measures. By providing a comprehensive and accurate approach to estimating the business cost of security attacks, big data analytics can help banks mitigate operational risks and improve their cybersecurity posture.