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Operational risk management: Implementing a Bayesian Network for foreign exchange and money market settlement

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  1. Introduction
    1. Motivation and Problem Description
    2. Objectives of the research
    3. Outline of the thesis
  2. Operational risk, foreign exchange and money market
    1. Basel II, operational risk
    2. Review of operational risk methods
    3. Models for quantification and capital allocation
    4. Loss distribution approach
    5. The scorecard or risk drivers and controls approach
    6. Assessment of Bayesian Networks for operational risk management
  3. Overview of Bayesian Networks
    1. Areas of application
    2. Theory of Bayesian Networks
    3. Strengths and limitations of Bayesian Networks
    4. Concluding remarks
  4. Introduction of a case study
    1. Special objectives in the case study
  5. Construction of a Bayesian Network for foreign exchange settlement
  6. Results, validation and applications
  7. Summary of the report
  8. Graphs and interpretation
  9. Bibliography
  10. Conclusion

Recent Financial scandals in the banking industry have caused considerable attention to be focused on operational risk. This is because an analysis of some of these scandals reveals that the underlying causes of these huge Financial losses are due to Operational Risk (OR) and not to credit or market risk, as might initially appeared to be the case. The Foreign Exchange (FX) market has had its fair share of these recent scandals. Two most recent examples of operational risk losses in the FX markets are the National Australia Bank's 227 million USD loss in 2004 and Allied Irish Bank's 750 million USD loss in 2002. These losses have had serious negative impact on the firms' profitability and reputation. Besides scandalous losses in the FX market, trade processing and settlement errors, as well as incorrect settlement, if not controlled, may lead to indirect costs such as compensation payments to counter parties, or to the development of large losses in a firm's portfolio due to managing the wrong position. Operational risk losses in the financial industry usually occur at the business unit level and are due to weak management oversight, weak internal controls or the lack of it, or to breakdown of procedures among others. Operational risk therefore has to be managed at the business unit level.

[...] List of Abbreviations ACI International Financial Markets Association AIB Allied Irish Bank AMA Advanced Measurement Approach BIS Bank for International Settlement BN Bayesian Network CFP Committee for Professionalism ACI CLS Continuously Linked Settlement CPT Conditional Probability Tables CSA Control Self Assessment DAG Directed Acyclic Graph FX Foreign Exchange KRI Key Risk Indicator LDA Loss Distribution Approach MCMC Markov Chain Monte -Carlo Simulation MM Money Market NAB National Australian Bank NDT Node Description Table OpVaR Operational Value at Risk OR Operational Risk OTC Over The Counter SI Settlement Instructions SOX Sarbanes Oxley Act STP Straight Through Process VaR Value at Risk Currencies AUD Australian dollar BRL Brazilian real CAD Canadian dollar CHF Swiss franc CZK Czech koruna DKK Danish krone EUR Euro GBP Great Britain Pound Sterling HKD Hong Kong dollar HUF Hungarian forint JPY Japanese yen MXN Mexican peso NOK Norwegian krone NZD New Zealand dollar PLN Polish zloty RUB Russian ruble SEK Swedish krona SGD Singapore dollar SKK Slovak koruna TRL Turkish Lira USD American dollar ZAR South African rand CHAPTER 1 Introduction and Motivation 1.1 Introduction Recent Financial scandals in the banking industry have caused considerable attention to be focused on operational risk. [...]


[...] A survey on algorithms for real-time Bayesian network infer ence. In the joint AASI-02/KDD-02/UAI-02 workshop on Real-Time Decision Support and Diagnosis Systems O. Hansson and A. Mayer. Heuristic search as evidential reasoning. In Proceedings of the 5th Workshop on Uncertainty in Artificial Intelligence M. Haubenstock. The operational risk management framework. In C. Alexander, editor, Operational risk, regulation, analysis and management, London Pearson Education. D. Heckermann. Bayesian networks for data mining. Data mining and knowledge discovery, 1:79{ D. Heckermann and J.S. [...]


[...] CONSTRUCTION OF A BAYESIAN NETWORK FOR FOREIGN EXCHANGE SETTLEMENT Testing The purpose of the test run is twofold: to obtain feedback information that can be used to revise the real elicitation and to give training to the project personnel (elicitor). Testing was carried out on one expert as mentioned earlier. Three components of the elicitation process were tested. These are introduction given to the experts, the elicitation procedures, and lastly the entry of data into the model for analysis or end use. [...]

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