The measurement of operational risk has surely been one of the biggest challenges for banks worldwide. Most banks worldwide have opted for a value-at-risk (VaR) approach, based on the success achieved with market risk, to measure and quantify operational risk. The problem banks have is that they do not always find it difficult to calculate this VaR figure, as there are numerous mathematical and statistical methods and models that can calculate VaR, but they struggle to understand and interpret the values that are produced by VaR models and methods. Senior management and normal staff do not always understand how these VaR values will impact their decision-making and they do not always know how to incorporate these values in their day-to-day management of the bank. This study therefore aims to explain and discuss the calculation of VaR for operational risk as well as the factors that influence this figure, and then also to discuss how this figure is managed and the impact that it has on the management of a bank. The main goal of this study is then to explain the management of VaR for operational risk in order to understand how it can be incorporated in the overall management of a bank. The methodology used includes a literature review, in-depth interviews and a case study on a South African Retail Bank to determine and evaluate some of the most renowned methods for calculating VaR for operational risk. The first objective of this study is to define operational risk and all its elements in order to distinguish it from all the other risks the banking industry faces and to better understand the management thereof. It is the view of this study that it will be impossible to manage and measure operational risk if it is not clearly defined, and it is therefore important to have a clear and understandable definition of operational risk. The second objective is to establish an operational risk management process that will ensure a structured approach to the management of operational risk, by focusing on the different phases of operational risk. The process discussed by this study is a combination of some of the most frequent used processes by international banks, and is intended to guide the reader in terms of the steps required for managing operational risk. The third objective of this study is to discuss and explain the qualitative factors that play a role in the management of operational risk, and to determine where these factors fit into the operational risk process and the role they play in calculating the VaR for operational risk. These qualitative factors include, amongst others, key risk indicators (KRIs), risk and control self-assessments and the tracking of operational losses. The fourth objective is to identify and evaluate the quantitative factors that play a role in the management of operational risk, to distinguish these factors from the qualitative factors, and also to determine where these factors fit into the operational risk management process and the role they play in calculating VaR for operational risk. Most of these quantitative factors are prescribed by the Base1 Committee by means of its New Capital Accord, whereby this new framework aims to measure operational risk in order to determine the amount of capital needed to safeguard a bank against operational risk. The fifth objective is to discuss and explain the calculation of VaR for operational risk by means of discussing all the elements of this calculation. This study mainly bases its discussion on the loss distribution approach (LDA), where the frequency and severity of operational loss events are convoluted by means of Monte Carlo simulations. This study uses real data obtained from a South African Retail Bank to illustrate this calculation on a practical level. The sixth and final objective of this study is to explain how VaR for operational risk is interpreted in order for management to deal with it and make proper management decisions based on it. The above-mentioned discussion is predominantly based on the two types of capital that are influenced by VaR for operational risk. / Thesis (Ph.D. (Risk Management))--North-West University, Potchefstroom Campus, 2007.

The measurement of operational risk has surely been one of the biggest challenges for banks worldwide. Most banks worldwide have opted for a value-at-risk (VaR) approach, based on the success achieved with market risk, to measure and quantify operational risk. The problem banks have is that they do not always find it difficult to calculate this VaR figure, as there are numerous mathematical and statistical methods and models that can calculate VaR, but they struggle to understand and interpret the values that are produced by VaR models and methods. Senior management and normal staff do not always understand how these VaR values will impact their decision-making and they do not always know how to incorporate these values in their day-to-day management of the bank. This study therefore aims to explain and discuss the calculation of VaR for operational risk as well as the factors that influence this figure, and then also to discuss how this figure is managed and the impact that it has on the management of a bank. The main goal of this study is then to explain the management of VaR for operational risk in order to understand how it can be incorporated in the overall management of a bank. The methodology used includes a literature review, in-depth interviews and a case study on a South African Retail Bank to determine and evaluate some of the most renowned methods for calculating VaR for operational risk. The first objective of this study is to define operational risk and all its elements in order to distinguish it from all the other risks the banking industry faces and to better understand the management thereof. It is the view of this study that it will be impossible to manage and measure operational risk if it is not clearly defined, and it is therefore important to have a clear and understandable definition of operational risk. The second objective is to establish an operational risk management process that will ensure a structured approach to the management of operational risk, by focusing on the different phases of operational risk. The process discussed by this study is a combination of some of the most frequent used processes by international banks, and is intended to guide the reader in terms of the steps required for managing operational risk. The third objective of this study is to discuss and explain the qualitative factors that play a role in the management of operational risk, and to determine where these factors fit into the operational risk process and the role they play in calculating the VaR for operational risk. These qualitative factors include, amongst others, key risk indicators (KRIs), risk and control self-assessments and the tracking of operational losses. The fourth objective is to identify and evaluate the quantitative factors that play a role in the management of operational risk, to distinguish these factors from the qualitative factors, and also to determine where these factors fit into the operational risk management process and the role they play in calculating VaR for operational risk. Most of these quantitative factors are prescribed by the Base1 Committee by means of its New Capital Accord, whereby this new framework aims to measure operational risk in order to determine the amount of capital needed to safeguard a bank against operational risk. The fifth objective is to discuss and explain the calculation of VaR for operational risk by means of discussing all the elements of this calculation. This study mainly bases its discussion on the loss distribution approach (LDA), where the frequency and severity of operational loss events are convoluted by means of Monte Carlo simulations. This study uses real data obtained from a South African Retail Bank to illustrate this calculation on a practical level. The sixth and final objective of this study is to explain how VaR for operational risk is interpreted in order for management to deal with it and make proper management decisions based on it. The above-mentioned discussion is predominantly based on the two types of capital that are influenced by VaR for operational risk. / Thesis (Ph.D. (Risk Management))--North-West University, Potchefstroom Campus, 2007.

Oil/gas and petrochemical plants are complicated and dynamic in nature. Dynamic characteristics include ageing of equipment/components, season changes, stochastic processes, operator response times, inspection and testing time intervals, sequential dependencies of equipment/components and timing of safety system operations, all of which are time dependent criteria that can influence dynamic processes. The conventional risk assessment methodologies can quantify dynamic changes in processes with limited capacity. Therefore, it is important to develop method that can address time-dependent effects. The primary objective of this study is to propose a risk assessment methodology for dynamic systems. In this study, a new technique for dynamic operational risk assessment is developed based on the Bayesian networks, a structure optimal suitable to organize cause-effect relations. The Bayesian network graphically describes the dependencies of variables and the dynamic Bayesian network capture change of variables over time. This study proposes to develop dynamic fault tree for a chemical process system/sub-system and then to map it in Bayesian network so that the developed method can capture dynamic operational changes in process due to sequential dependency of one equipment/component on others. The developed Bayesian network is then extended to the dynamic Bayesian network to demonstrate dynamic operational risk assessment. A case study on a holdup tank problem is provided to illustrate the application of the method. A dryout scenario in the tank is quantified. It has been observed that the developed method is able to provide updated probability different equipment/component failure with time incorporating the sequential dependencies of event occurrence. Another objective of this study is to show parallelism of Bayesian network with other available risk assessment methods such as event tree, HAZOP, FMEA. In this research, an event tree mapping procedure in Bayesian network is described. A case study on a chemical reactor system is provided to illustrate the mapping procedure and to identify factors that have significant influence on an event occurrence. Therefore, this study provides a method for dynamic operational risk assessment capable of providing updated probability of event occurrences considering sequential dependencies with time and a model for mapping event tree in Bayesian network.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Summaries in English, Afrikaans and Zulu / The significance for a bank to determine its risk appetite has become crucial over the years, based on past and recent risk events in the financial services sector. Regulatory pressure, a focus on corporate governance and risk management have been stimuli for many changes in the financial industry. An example is the need to establish an operational risk appetite framework. It is against this background that the study aimed to identify guiding principles for an operational risk appetite framework that can be used to determine the operational risk appetite of a bank. The study entailed a literature review and an empirical analysis of the principles for an operational risk appetite framework for the banking industry of South Africa. A survey was used to collate the data. Also, the researcher endeavoured to establish a gap between the principles and the current status of implementation of the confirmed principles. The descriptive and inferential results indicated that most of the identified principles were viewed as important and crucial for an operational risk appetite framework for a South African bank, although some were not yet fully implemented. The study also confirmed the principles for an effective operational risk appetite framework to comply with regulatory requirements and to ensure a sound risk management process to support the achievement of business objectives. / Dat 'n bank in staat is om sy risikoaptyt in die finansiëledienstesektor vas te stel, is betekenisvol en dit het oor jare heen vanweë vorige en onlangse risikogebeurtenisse van kritieke belang geword. Die druk van regulering, 'n fokus op korporatiewe bestuur en risikobestuur is stimuli vir talle veranderinge in die finansiële bedryf. 'n Voorbeeld hiervan is die noodsaaklikheid daarvan om 'n operasionele risikoaptytraamwerk op te stel. Teen hierdie agtergrond het die studie beoog om riglyne te identifiseer vir 'n operasionele risikoaptytraamwerk wat gebruik kan word om 'n bank se operasionele risikoaptyt te bepaal. Die studie omvat ’n literatuuroorsig en ’n empiriese ontleding van die beginsels van ’n operasionele risikoaptytraamwerk vir die bankbedryf in Suid-Afrika. ’n Opname is gebruik om die ingesamelde data te vergelyk, en die navorser het gepoog om ’n leemte tussen die beginsels en die huidige stand van implementering van die bevestigde beginsels uit te wys. In die beskrywende en inferensiële resultate word aangedui dat die meeste van die geïdentifiseerde beginsels beskou word as belangrik en kritiek vir ’n operasionele risikoaptytraamwerk vir ’n Suid-Afrikaanse bank, al word sommige beginsels nog nie ten volle geïmplementeer nie. Die studie bevestig die beginsels van ’n effektiewe operasionele risikoaptytraamwerk met die oog daarop om aan reguleringsvereistes te voldoen en ’n deurdagte risikobestuursproses te verseker en sodoende die verwesenliking van sakedoelwitte te ondersteun. / Ngokuhamba kweminyaka kuya kubaluleka kakhulu ukuba ibhanki iwujonge ngononophelo umngcipheko enokuwuthatha, ngenxa yokubona iziganeko zomngcipheko ezenzekileyo kwicandelo leenkonzo zoqoqosho. Uxinzelelo lolawulo, ugxininiso kulawulo lweenkampani kunye nolawulo lomngcipheko zizinto eziphembelele iinguqu ezininzi kurhwebo lokwenza imali. Umzekelo sisidingo sokuseka uphahla lokusebenza ngomngcipheko. Zezi zinto ezibangela ukuba esi sifundo sijolise ekufumaniseni iinqobo ezisisikhokelo sokuqwalasela umngcipheko onokuthathwa, nesinokusetyenziselwa ukulinganisela umngcipheko onokuthathwa yibhanki. Esi sifundo siphengulule uluncwadi olukhoyo ngalo mbandela kunye nohlalutyo olunobungqina lweenqobo ezinokusetyenziselwa ukulinganisela umngcipheko onokuthathwa licandelo leebhanki zoMzantsi Afrika. Kwenziwa uhlolo zimvo ekuqokeleleni iinkcukacha zolwazi. Umphandi wabuya wazama ukubonisa umahluko phakathi kweenqobo ezimiselweyo nemeko ekuyiyo ekusetyenzisweni kweenqobo ezivunyiweyo. Iziphumo ezichazayo nezicingelwayo zibonise ukuba uninzi lweenqobo zibonwa njengamanqaku abalulekileyo nangundoqo okwenza uphahla lokusebenza ngomngcipheko onokuthathwa yibhanki eMzantsi Afrika, nangona ezinye zingekasetyenziswa ngokupheleleyo. Esi sifundo siphinde sangqinisisa iinqobo zophahla olululo lokusebenza ngomngcipheko onokuthathwa ezimele ukuthobela imigaqo elawulayo nokuqinisekisa inkqubo yomgcipheko eqinileyo yokuxhasa ukufunyanwa kweenjongo zoshishino. / Business Management / M. Com. (Business Management)

Operational risk has become an increasingly important topic within financial institutions of late, resulting in an increased spend by financial service organisations on operational risk management solutions. While this move is positive, evidence has shown that information technology implementations have tended to have low rates of success. Research highlighted that a series of defined critical success factors could reduce the risk of implementation failure. Investigations into the literature revealed that no critical success factors had been defined for the implementation of an operational risk management system. Through a literature study, a list of 29 critical success factors was identified. To confirm these factors, a questionnaire was developed. The questionnaire was distributed to an identified target audience within the South African financial services community. Reponses to the questionnaire revealed that 27 of the 29 critical success factors were deemed important and critical to the implementation of an operational risk management system. / Business Management / M. Com. (Business Management)

Operational risk has become an increasingly important topic within financial institutions of late, resulting in an increased spend by financial service organisations on operational risk management solutions. While this move is positive, evidence has shown that information technology implementations have tended to have low rates of success. Research highlighted that a series of defined critical success factors could reduce the risk of implementation failure. Investigations into the literature revealed that no critical success factors had been defined for the implementation of an operational risk management system. Through a literature study, a list of 29 critical success factors was identified. To confirm these factors, a questionnaire was developed. The questionnaire was distributed to an identified target audience within the South African financial services community. Reponses to the questionnaire revealed that 27 of the 29 critical success factors were deemed important and critical to the implementation of an operational risk management system. / Business Management / M. Com. (Business Management)

With few previous data and literature based on the South African banking sector, the key aim of this study was to contribute further results concerning the effect of operational loss events on the reputation of South African banks. The main distinction between this study and previous empirical research is that a small sample of South African banks listed on the JSE, between 2000 and 2014 was used. Insurance companies fell outside the scope of the study. The study primarily focused on identifying reputational risk among Regal Treasury Bank, Saambou Bank, African Bank and Standard Bank. The events announced by these banks occurred between 2000 and 2014. The precise date of the announcement of the operational events was also determined. Stock price data were collected for those banks that had unanticipated operational loss announcements (i.e. the event). Microsoft Excel models applied to the reputational loss as the difference between the operational loss announcement and the loss in the stock returns of the selected banks. The results indicated significant negative abnormal returns on the announcement day for three of the four banks. For one of the banks it was assumed that the operational loss was not significant enough to cause reputational risk. The event methodology similar to previous literature, furthermore examined the behaviour of return volatility after specific operational loss events using the sample of banks. The study further aimed at making two contributions. Firstly, to analyse return volatility after operational loss announcements had been made among South African banks, and secondly, to compare the sample of affected banks with un-affected banks to further identify whether these events spilled over into the banking industry and the market. The volatility of these four banks were compared to three un-affected South African banks. The results found that the operational loss events for Regal Treasury Bank and Saambou Bank had no influence on the unaffected banks. However the operational loss events for African Bank and Standard Bank influenced the sample of unaffected banks and the Bank Index, indicating systemic risk.

With few previous data and literature based on the South African banking sector, the key aim of this study was to contribute further results concerning the effect of operational loss events on the reputation of South African banks. The main distinction between this study and previous empirical research is that a small sample of South African banks listed on the JSE, between 2000 and 2014 was used. Insurance companies fell outside the scope of the study. The study primarily focused on identifying reputational risk among Regal Treasury Bank, Saambou Bank, African Bank and Standard Bank. The events announced by these banks occurred between 2000 and 2014. The precise date of the announcement of the operational events was also determined. Stock price data were collected for those banks that had unanticipated operational loss announcements (i.e. the event). Microsoft Excel models applied to the reputational loss as the difference between the operational loss announcement and the loss in the stock returns of the selected banks. The results indicated significant negative abnormal returns on the announcement day for three of the four banks. For one of the banks it was assumed that the operational loss was not significant enough to cause reputational risk. The event methodology similar to previous literature, furthermore examined the behaviour of return volatility after specific operational loss events using the sample of banks. The study further aimed at making two contributions. Firstly, to analyse return volatility after operational loss announcements had been made among South African banks, and secondly, to compare the sample of affected banks with un-affected banks to further identify whether these events spilled over into the banking industry and the market. The volatility of these four banks were compared to three un-affected South African banks. The results found that the operational loss events for Regal Treasury Bank and Saambou Bank had no influence on the unaffected banks. However the operational loss events for African Bank and Standard Bank influenced the sample of unaffected banks and the Bank Index, indicating systemic risk.

This research shed the light on regulations and their effects on operational risk management within the Swedish Swedish Banking Sector. The focus lies on operational risk management due to the introduction of new regulations such as FFFS 2014:1, FFFS 2014:4 and FFFS 2014:5. What could be found in the empirical analysis is that the regulations affected organizational changes.  Additionally, differences between large and small banks could be seen. All changes in terms of implementation of regulations are strongly performed throughout the Three Lines of Defence model where clear organization structure and work description are of importance. The Three Lines of Defence is tightly combined with the COSO framework and operational risk management to conduct compliant organization that is adaptable for any regulatory changes.