Implementing the AMA model

The researcher maintains institutions must use appropriate criteria for data collection in line with the ITWG financial institution requirements. However, distinguishing loss events remains a critical aspect of the data collection strategy. The author maintains that there are more issues highlighted under the ITWG guidelines in regard to data security, collection, impact on taxes and completeness among others. Data insufficiency has been identified as one of the greatest challenge under this framework.

As such, firms and financial institutions have been forced to supplement their AMA model by use of external data collection strategies. This includes the use of vendors who have a deeper understanding of the general trends. The researcher critically explores the challenges faced in implementing the AMA model. One of such challenges pertains to data accuracy and completeness which plays a critical role in enhancing risk management strategies. Setting of data threshold has also been identified as a significant challenge in implementing the AMA model.

The incorporation of external data has been credited with enabling completion of data capture in the event such data is lost as well as the modification of parameters used in internal loss of data. The author maintains that this helps in enhancing quality and credibility of external data hence leading to increased scalability band relevance of data. The section on scenario analysis highlights the ITWG definition of scenario analysis as the expert forecasting of operational risk loses. As such, it is very clear that the scenario analysis enables the generation of additional data to complete frequency and severity.

As such, there are three kinds of data loses identified under this section. These are unexpected data loss causing severe and catastrophic scenario, unexpected loss of data leading to pessimistic scenarios and expected loss leading to optimistic scenarios. The author examines the scenario based AMA also referred to as the sb AMA. As such, it is notable that scenarios have the potential for influencing future management strategies and therefore must be evaluated to determine the prevalent potential frequency of operational risks as well as the associated severity of the risks.

The author approaches scenario analysis from the perspective of a case study of the Federal Reserve Bank in the US and the American framework for quantifying operational risks. Due to increased need for operational frameworks for analyzing operational risks, financial institutions have been compelled to address the challenges emanating from internal data loss and the use of new data quantification methods like the Value-at-Risk model that relies on computation outcomes (NYFRB 1996). This includes frameworks for creating standard procedures for different business lines within financial institutions.

In addition, the writer examines literature by Kabir Dutta and Jason Perry of January 2007 which discussed the problems financial institutions faced in choosing operating models. The author argues that the LDA enhances frequency and severity of data collection in the event of loss. The performance measures used include good fit statistics, flexibility and simplicity. In chapter four, the author examines the methodologies for calculating capital chargers including the basic indicator approach, the standardized approach and the advanced measurement approach.

In addition, the author critically explores Basic Indicator Approach and its application in calculating amount of operational risk. Similarly, the author looks at the scorecard approach and its application in enhancing the accuracy of operational risk measurement. As such the author gives deep analyses of the practical use of loss distribution approach as well as the severity of estimation and capital charge calculation and estimation. Under this chapter, the author reveals the results of the questioners, experience of respondents and the actual findings.

In chapter five, the researcher gives his conclusions based on the study revelations as well as the recommendations for improvement in operational risk management. As such, it is imperative that operational risk management enables financial institutions to mitigate loses due to operational risks. Basel two remains an important development which is ideally an improvement of the existing framework for operational risk measurement and evaluation. The score card approach is also an effective tool for risk management and estimation of the capital charge.

The author recommends continuous risk assessment and monitoring based on the Basel regulations to help financial institutions cope with the vulgarity of the global economic instabilities. In this regard, the author argues that educating management officers and relevant organizational teams enhances operational risk measurement and assessment. However, the author also raises concerns about the benefits of implementing the Basel accord by arguing that compliance with the Basel accord could lead to increased cases of defaults in loan payments due to using supplementary methods like the KRI.

This is based on the finding that 62% of participants in the study approve the accord in its ability to stabilize global financial markets while 40% believed that this tool could not prevent global financial problems. Bibliography Barth, J. , Caprio, G. , Levine, R. (2001)Bank Regulation and Supervision: What Works Best Available: Bank Regulation and Supervision: What Works Best Available: http://siteresources. worldbank. org/DEC/Resources/84797-1251813753820/64157391251814028691/What_Works_Best.

pdf Last accessed 2 April 2010 Basel Committee on Banking Supervision. (2000). Range of Practice in Banks’ Internal Ratings Systems. Available: www. bis. org/publ/bcbs66. pdf. Last accessed 2 April 2010. BITS. (2004). Developing a KRI Program : Guidance for the Operational Risk Manager. Available: http://www. bits. org/downloads/Publications%20Page/bitskriprog. doc. Last accessed 2 April 2010. Boudoukh, J. , Richardson, M. , Whitelaw, R. (1995). Expect the Worst . London: Risk Publications. p101 .