scholarly journals Operational Risks in Financial Sectors

2012 ◽  
Vol 2012 ◽  
pp. 1-57
Author(s):  
E. Karam ◽  
F. Planchet
Keyword(s):  
Real Life ◽  
Operational Risk ◽  
Financial Sector ◽  
Solvency Ii ◽  
Basel Ii ◽  
Insurance Companies ◽  
Operational Risks

A new risk was born in the mid-1990s known as operational risk. Though its application varied by institutions—Basel II for banks and Solvency II for insurance companies—the idea stays the same. Firms are interested in operational risk because exposure can be fatal. Hence, it has become one of the major risks of the financial sector. In this study, we are going to define operational risk in addition to its applications regarding banks and insurance companies. Moreover, we will discuss the different measurement criteria related to some examples and applications that explain how things work in real life.

scholarly journals Cyber operational risk scenarios for insurance companies

2019 ◽  
Vol 24 ◽  
Author(s):  
R. Egan ◽  
S. Cartagena ◽  
R. Mohamed ◽  
V. Gosrani ◽  
J. Grewal ◽  
...  
Keyword(s):  
Cyber Security ◽  
Operational Risk ◽  
Social Engineering ◽  
Insurance Companies ◽  
List Type ◽  
Operational Risks ◽  
Life Insurer ◽  
Cyber Risk ◽  
The Impact ◽  
Over Time

AbstractCyber Operational Risk: Cyber risk is routinely cited as one of the most important sources of operational risks facing organisations today, in various publications and surveys. Further, in recent years, cyber risk has entered the public conscience through highly publicised events involving affected UK organisations such as TalkTalk, Morrisons and the NHS. Regulators and legislators are increasing their focus on this topic, with General Data Protection Regulation (“GDPR”) a notable example of this. Risk actuaries and other risk management professionals at insurance companies therefore need to have a robust assessment of the potential losses stemming from cyber risk that their organisations may face. They should be able to do this as part of an overall risk management framework and be able to demonstrate this to stakeholders such as regulators and shareholders. Given that cyber risks are still very much new territory for insurers and there is no commonly accepted practice, this paper describes a proposed framework in which to perform such an assessment. As part of this, we leverage two existing frameworks – the Chief Risk Officer (“CRO”) Forum cyber incident taxonomy, and the National Institute of Standards and Technology (“NIST”) framework – to describe the taxonomy of a cyber incident, and the relevant cyber security and risk mitigation items for the incident in question, respectively.Summary of Results: Three detailed scenarios have been investigated by the working party:∙Employee leaks data at a general (non-life) insurer: Internal attack through social engineering, causing large compensation costs and regulatory fines, driving a 1 in 200 loss of £210.5m (c. 2% of annual revenue).∙Cyber extortion at a life insurer: External attack through social engineering, causing large business interruption and reputational damage, driving a 1 in 200 loss of £179.5m (c. 6% of annual revenue).∙Motor insurer telematics device hack: External attack through software vulnerabilities, causing large remediation / device replacement costs, driving a 1 in 200 loss of £70.0m (c. 18% of annual revenue).Limitations: The following sets out key limitations of the work set out in this paper:∙While the presented scenarios are deemed material at this point in time, the threat landscape moves fast and could render specific narratives and calibrations obsolete within a short-time frame.∙There is a lack of historical data to base certain scenarios on and therefore a high level of subjectivity is used to calibrate them.∙No attempt has been made to make an allowance for seasonality of renewals (a cyber event coinciding with peak renewal season could exacerbate cost impacts)∙No consideration has been given to the impact of the event on the share price of the company.∙Correlation with other risk types has not been explicitly considered.Conclusions: Cyber risk is a very real threat and should not be ignored or treated lightly in operational risk frameworks, as it has the potential to threaten the ongoing viability of an organisation. Risk managers and capital actuaries should be aware of the various sources of cyber risk and the potential impacts to ensure that the business is sufficiently prepared for such an event. When it comes to quantifying the impact of cyber risk on the operations of an insurer there are significant challenges. Not least that the threat landscape is ever changing and there is a lack of historical experience to base assumptions off. Given this uncertainty, this paper sets out a framework upon which readers can bring consistency to the way scenarios are developed over time. It provides a common taxonomy to ensure that key aspects of cyber risk are considered and sets out examples of how to implement the framework. It is critical that insurers endeavour to understand cyber risk better and look to refine assumptions over time as new information is received. In addition to ensuring that sufficient capital is being held for key operational risks, the investment in understanding cyber risk now will help to educate senior management and could have benefits through influencing internal cyber security capabilities.

scholarly journals Assessing operational risk while using the logic of the included middle

2019 ◽  
Vol 13 (1) ◽  
pp. 1204-1215
Author(s):  
Răzvan Tudor
Keyword(s):  
Quantum Physics ◽  
Operational Risk ◽  
Solvency Ii ◽  
Risk Identification ◽  
Risk Profile ◽  
Topological Properties ◽  
Wave Mechanics ◽  
Operational Risks ◽  
Definition Of ◽  
Included Middle

Abstract From the Solvency II perspective, the capital requirement for operational risk is based on the application of a standard formula. The limitation imposed by this approach as well as the definition of operational risk by establishing certain types of activities (i.e. internal processes, people, systems, etc.) as generating causes does not allow, at least for the time being, the establishment of an effective way of managing the operational risk regardless of the type of strategy chosen. Any human operator involved in the risk identification and evaluation processes, within most of the organizations, would use the logic of the included middle based on Boolean binary values (i.e. true/false, 1/0, etc.). This article attempts to logically analyze the methodological impact that would result from using a logic of the included middle which accepts that an identified operational risk and an unidentified operational risk may coexist at the same time, in a risk profile, provided that the identified one is actual and the unidentified one is potential, reciprocal and alternative but never up to the 100% limit. The included middle in this approach is the transition state, which is perfectly possible in terms of defining the topological properties of the time in which the identified operational risks analyzed are assessed. The novelty of this approach is based on the fact that the logic of the included middle, which we include in research as a concept and as a tool, was one of the nudging factors that underpinned the development of the wave mechanics (e.g. Schrodinger’s Cat Paradox) and some of the quantum physics theories later, and its use has never been tested in risk management.

scholarly journals Operational risk quantification and modelling within Romanian insurance industry

2017 ◽  
Vol 11 (1) ◽  
pp. 637-648
Author(s):  
Răzvan Tudor ◽  
Dumitru Badea
Keyword(s):  
Standard Model ◽  
Insurance Industry ◽  
Operational Risk ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Capital Requirement ◽  
Risk Capital ◽  
Operational Risks ◽  
The Standard Model ◽  
Solvency Capital

Abstract This paper aims at covering and describing the shortcomings of various models used to quantify and model the operational risk within insurance industry with a particular focus on Romanian specific regulation: Norm 6/2015 concerning the operational risk issued by IT systems. While most of the local insurers are focusing on implementing the standard model to compute the Operational Risk solvency capital required, the local regulator has issued a local norm that requires to identify and assess the IT based operational risks from an ISO 27001 perspective. The challenges raised by the correlations assumed in the Standard model are substantially increased by this new regulation that requires only the identification and quantification of the IT operational risks. The solvency capital requirement stipulated by the implementation of Solvency II doesn’t recommend a model or formula on how to integrate the newly identified risks in the Operational Risk capital requirements. In this context we are going to assess the academic and practitioner’s understanding in what concerns: The Frequency-Severity approach, Bayesian estimation techniques, Scenario Analysis and Risk Accounting based on risk units, and how they could support the modelling of operational risk that are IT based. Developing an internal model only for the operational risk capital requirement proved to be, so far, costly and not necessarily beneficial for the local insurers. As the IT component will play a key role in the future of the insurance industry, the result of this analysis will provide a specific approach in operational risk modelling that can be implemented in the context of Solvency II, in a particular situation when (internal or external) operational risk databases are scarce or not available.

scholarly journals Scenario Analysis Approach for Operational Risk in Insurance Companies

2020 ◽  
Vol 14 (2) ◽  
pp. 153-165
Author(s):  
Michal Vyskočil
Keyword(s):  
Monte Carlo Simulation ◽  
Operational Risk ◽  
Solvency Ii ◽  
Typical Case ◽  
Insurance Companies ◽  
Insurance Company ◽  
Worst Case ◽  
To Come ◽  
Real Scenario ◽  
The Impact

The article deals with the possibility of calculating the required capital in insurance companies allocated to operational risk under Solvency II regulation and the aim of this article is to come up with model that can be use in insurance companies for calculating operational risk required capital. In the article were discussed and compared the frequency and severity distributions where was chosen Poisson for frequency and Lognormal for severity. For the calculation, was used only the real scenario and data from small CEE insurance company to see the effect of the three main parameters (typical impact, Worst case impact and frequency) needed for building the model for calculation 99,5% VaR by using Monte Carlo simulation. Article comes up with parameter sensitivity and/or ratio sensitivity on calculating capital. From the database arose two conclusions related to sensitivity where the first is that the impact of frequency is much higher in the interval (0;1) than above the interval to calculated capital and second conclusion is Worst case and Typical Case ratio, where we saw that if the ratio is around 150 or higher the calculated capital is increasing faster that the ration increase demonstrated on the scenario calculation.

scholarly journals Good practice guide to setting inputs for operational risk models

2016 ◽  
Vol 22 (1) ◽  
pp. 68-108 ◽  
Author(s):  
P. O. J. Kelliher ◽  
M. Acharyya ◽  
A. Couper ◽  
K. Grant ◽  
E. Maguire ◽  
...  
Keyword(s):  
Scenario Analysis ◽  
Risk Mitigation ◽  
Good Practice ◽  
Operational Risk ◽  
Capital Allocation ◽  
Solvency Ii ◽  
Service Firms ◽  
Risk Models ◽  
Operational Risks ◽  
Loss Data

AbstractThis paper seeks to establish good practice in setting inputs for operational risk models for banks, insurers and other financial service firms. It reviews Basel, Solvency II and other regulatory requirements as well as publicly available literature on operational risk modelling. It recommends a combination of historic loss data and scenario analysis for modelling of individual risks, setting out issues with these data, and outlining good practice for loss data collection and scenario analysis. It recommends the use of expert judgement for setting correlations, and addresses information requirements for risk mitigation allowances and capital allocation, before briefly covering Bayesian network methods for modelling operational risks.

Basel II compliant mapping of operational risks

2007 ◽  
Vol 2 (1) ◽  
pp. 93-114
Author(s):  
Ingo Schäl ◽  
Wolfgang Stummer
Keyword(s):  
Basel Ii ◽  
Operational Risks

scholarly journals Systemic Risk and Insurance Regulation †

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 74 ◽  
Author(s):  
Fabiana Gómez ◽  
Jorge Ponce
Keyword(s):  
Systemic Risk ◽  
Formal Model ◽  
Public Support ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Insurance Companies ◽  
Prudential Regulation ◽  
High Exposure ◽  
Insurance Firms ◽  
Low Exposure

This paper provides a rationale for the macro-prudential regulation of insurance companies, where capital requirements increase in their contribution to systemic risk. In the absence of systemic risk, the formal model in this paper predicts that optimal regulation may be implemented by capital regulation (similar to that observed in practice, e.g., Solvency II ) and by actuarially fair technical reserve. However, these instruments are not sufficient when insurance companies are exposed to systemic risk: prudential regulation should also add a systemic component to capital requirements that is non-decreasing in the firm’s exposure to systemic risk. Implementing the optimal policy implies separating insurance firms into two categories according to their exposure to systemic risk: those with relatively low exposure should be eligible for bailouts, while those with high exposure should not benefit from public support if a systemic event occurs.

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