NEED A PERFECT PAPER? PLACE YOUR FIRST ORDER AND SAVE 15% USING COUPON:

Risk Assessment Methodology: Operational Risk Management

Risk Assessment Methodology: Operational Risk Management

Risk is inherent in any events since civilization as long as the occurrence of those events is associated with significant probability (Kenett & Raanan, 2011). Events that are associated with risks need to be managed to curb a risk or prevent its occurrence. There exist various methods of managing risks such as the operational risk management and CARVER model and Maritime Security Risk Analysis Model (MSRAM) (Acheson, 2007). This paper seeks to evaluate operational risk management. In achieving this, an evaluation of the application of the model is done by considering its target audience and its implementation in risk management.

In 2011, Ian defined Operational Risk Management (ORM) as a framework that defines operational risk and lays down principles and procedure for the identification, assessment, monitoring, control or mitigation of those risks. Often such process may necessitate the establishment of a committee to oversee the implementation of this framework. This framework is used in the management of operational risks. Operational risk refers to any risk that is not involved in credit and market programs for risk management. According to Girling (2013), operational risks may occur due to inadequate or failure of processes, systems or people. Moreover, operational risks may occur due to external events.

The origin of operation risk management can be attributed to the proposals of Basel II in June 1999. These proposals resulted in the publication of operation risks. However, Nicholas Leeson is considered as the author of operational risks with the risks being marshaled by scandals such as Daiwa and Barings (Power, 2005). Huber (2012) explains that the target for ORM is any asset management organizations. Operational risks are a critical concern for asset management companies and are one of the primary causes of failures in these organizations.

Application of ORM depends on a variety of factors that directly influence risk analysis. According to Ashraf et al. (2014), ORM depends on consistency, accuracy and completeness in operational risk data collection. Such data is indispensable in ensuring that ORM is holistic as well as in operational risk measurement. There exist various principles that guarantee the success of ORM. One of the principles is that there should be a strong culture id risk management in an organization. Moreover, a framework integrating an organization’s processes of risk management should be developed, implemented and maintained upon approval by an organization’s board. Moreover, an organization’s governance should review the framework and ensure that it is implemented.

Moreover, Blunden and Thirlwell (2013) give strategy as another element of ORM. The strategy for ORM in an organization should act as a guide for other activities involving risk tolerance, risk management processes and policies. Besides, this strategy should be dependent on the risk appetite of the organization. Other elements of ORM involve periodic evaluations depending on internal and external changes, structure and execution.

ORM has three lines of defense that include control and management system, risk management and internal audit in an ascending manner of their levels (Tattam, 2011). The management and control system is the business line that is an internal control system that covers all the operations in an organization. The risk management defense refers to oversight and defense activities that are responsible for operational risks identification, measurement, monitoring and reporting on an enterprise basis. The third line concerns with internal audit functions in an organization and is designed to improve the effectiveness of governance, control and risk management processes.

In 2012, Hubber explained that operational risks cause about 50% of failures in hedge funds. A similar account has been observed in the banking sector (Oesterreichische Nationalbank, & Finanzmarkt Austria Dienstleistungs GesmbH, 2006). Moreover, there has been more concern by management to make strategic decisions with a low likelihood of causing adverse effects (Samad-Khan, 2008). Samad-Khan further explains that ORM helps management to make such strategic decisions by incorporating risk-control and risk-reward in the decision-making processes.  The modern ORM transforms the risk data into loss severity and frequency to determine the expected and unexpected risk class loss for a given timeframe. Such valuations use actuarial loss model and historical loss data.

ORM uses actuarial science in its calculation of capital figures and is hence difficult (Global insurance center, 2008; Samad-Khan, 2008). Consequently, the application of the approach by an average security officer may be ambiguous and difficult. Moreover, the application of the approach may necessitate massive investments making it difficult to apply by small enterprises and average security officers.

ORM is an indispensable tool in the making of strategic decisions for a variety of reasons. The approach helps to free capital by allowing organizations to concentrate more on income-earning activities. Moreover, it contributes to minimization of volatility in an organization to improve its ability to meet profit and revenue targets. Moreover, the intense involvement of an organization’s executive boards helps to improve its regulatory compliance. However, ORM has got its drawbacks. For instance, operational risks as defined in Basel II vary both in nature and source in various organizations. For instance, the operational risks of the banking sector are not similar to those of the insurance sector. Moreover, the approach focuses on mitigating operational risks to prevent organizational failure. Consequently, ORM fails in considering other factors that may result in corporate failure that occurs in a chain of event nature triggered by operational risks.

 

 

References

Acheson, D. W. (2007). Vulnerability Assessment Tools. Global Food Safety and Quality Conference, Chicago, USA.

Ashraf, M. K., Zaman, S., Farooq, L. M., Jahangir, S. S. & Waqas, A. (2014). Implementation of Operational Risk Management Framework. Retrieved from http://www.sbp.org.pk/bprd/2014/C4-Annexure-1.pdf

Blunden, T., & Thirlwell, J. (2013). Mastering operational risk: A practical guide to understanding operational risk and how to manage it. Harlow, England: Pearson.

Girling, P. (2013). Operational risk management: A complete guide to a successful operational risk framework. Hoboken, N.J: Wiley.

Global insurance center (2008). Measuring operational risk. Retrieved from http://www.ey.com/Publication/vwLUAssets/Industry_Insurance_SolvencyII_Measuring_operational_risk/$file/Industry_Insurance_SolvencyII_Measuring_operational_risk.pdf

Huber, C. (2012). Operational Risk Management in Practice: Implementation, Success Fac-tors and Pitfalls. Risk and Finance, (34), 56-71.

Ian, S. (2011).Operational Risk Management and Business Continuity Planning for Modern State Treasuries. Washington, DC: International Monetary Fund.

Kenett, R., & Raanan, Y. (Eds.). (2011). Operational Risk Management: a practical approach to intelligent data analysis (Vol. 106). John Wiley & Sons.

Oesterreichische Nationalbank, & Finanzmarkt Austria Dienstleistungs GesmbH. (2006). Guidelines on Operational Risk Management. OeNB and FMA.

Power, M. (2005). The invention of operational risk. Review of International Political Economy12(4), 577-599.

Samad-Khan, A. (2008). Modern operational risk management. Emphasis2, 26-29.

Tattam, D. (2011). A Short Guide to Operational Risk. Gower Publishing, Ltd..

Looking for this or a Similar Assignment? Click below to Place your Order Instantly!