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This study on the insurance sector’s digital transformation highlights a paradox: adopting technologies like cloud computing, AI, and IoT enhances efficiency but increases cybersecurity risks. A survey of 150 professionals and interviews with 15 executives show a strong correlation (r = .78, p < .01) between digital technology use and security incidents, with phishing (88%), cloud misconfigurations (45%), and IoT vulnerabilities (25%) prevalent. Traditional defenses score high (e.g., network perimeter: 4.1/5), but IoT and software supply chain security lag (2.4–2.7/5). Cyber insurance now uses dynamic risk assessments, with 90% of underwriters employing external security ratings and 75% applying surcharges for high-risk technologies.

The provided text is an **academic article** that offers a comprehensive **analytical review of cyber risk management** within the insurance industry, focusing heavily on the **mathematical models** used for risk quantification and premium pricing. The review systematically covers the current state-of-the-art in cyber risk, discussing how dynamic and interconnected threats challenge traditional actuarial methods, necessitating the use of advanced quantitative tools like **stochastic models and copulas** to manage dependencies and calculate **Solvency Capital Requirements (SCR)**. It thoroughly details various **vulnerability functions** (including the well-known Gordon-Loeb model and its extensions) and different **premium calculation principles** (such as Expected Value and Mean-Variance), concluding that closer collaboration between different disciplines is essential for developing **robust cyber insurance and reinsurance solutions** in an increasingly digital landscape.

The report discusses the growing threat of cyber risk to the EU's financial stability. Key points include:

• Cyber risk is a significant and systemic threat to the EU's financial sector, with increasing frequency and sophistication of attacks.

• Factors amplifying risk include geopolitical tensions, third-party IT dependencies, and the dual-edged impact of AI.

• The financial sector, including banks and insurers, faces tangible impacts from cyber threats.

• DORA is seen as a critical step requiring ongoing commitment to vigilance and resource allocation for digital infrastructure defense.

Financial institutions are increasingly dependent on third-party service providers (TPSPs), raising concerns about systemic risks due to limited transparency. While the EU and U.K. have introduced formal oversight regimes, the U.S. relies on industry cooperation and micro-prudential supervision. A recent case study highlights financial stability risks from a payments disruption linked to a TPSP. As rapid technological change reshapes the financial sector, vulnerabilities from TPSP concentration and interconnectedness may grow. Greater understanding is needed to assess these risks and inform potential oversight responses.

The Cyber Due Diligence Object Model (CDDOM) is a structured, extensible framework designed for SMEs to manage cybersecurity due diligence in digital supply chains. Aligned with regulations like NIS2, DORA, CRA, and GDPR, CDDOM enables continuous, automated, and traceable due diligence. It integrates descriptive schemas, role-specific messaging, and decision support to facilitate supplier onboarding, risk reassessment, and regulatory compliance. Validated in real-world scenarios, CDDOM supports automation, transparency, and interoperability, translating compliance and trust signals into machine-readable formats. It fosters resilient, decision-oriented cyber governance, addressing modern cybersecurity challenges outlined in recent research.

This study extends the Gordon–Loeb model for cybersecurity investment by incorporating a Hawkes process to model temporally clustered cyberattacks, reflecting real-world attack bursts. Formulated as a stochastic optimal control problem, it maximizes net benefits through adaptive investment policies that respond to attack arrivals. Numerical results show these dynamic strategies outperform static and Poisson-based models, which overlook clustering, especially in high-risk scenarios. The framework aids risk managers in tailoring responsive cybersecurity strategies. Future work includes empirical calibration, risk-averse loss modeling, cyber-insurance integration, and multivariate Hawkes processes for diverse attack types.

The OCC reports that operational risk is elevated due to cyber threats and complex operations. Compliance risks are also significant, especially in areas like BSA/AML and fraud prevention. External fraud targeting consumers and banks is increasing, requiring strong fraud management practices. Banks should prioritize risk management, maintain sound controls, and educate customers to mitigate these risks.

This paper analyzes the constraints on the #insuranceindustry in providing larger capacity for #cyberrisk #insurance. The authors argue that cyber risk is unique in that it is both information-intensive to underwrite and heavy-tailed, leading to a tension between the need to raise large amounts of external capital to finance heavy-tailed risks and the high compensation demanded by capital providers due to information frictions.

While previous research has focused on #cyberrisk #riskmitigation measures, this study describes the emergence of various real-world cyber #risktransfer products in the last decade, including #warranties, #cloudcomputing partnerships, #parametricinsurance, #reinsurance, and #cyber #catbonds.

We provide a #cyberrisk definition and classification scheme for #riskmanagement purposes, to be used as a data collection template for #financialinstitutions.