Thesis (Ph. D.)--UCLA, 2009. / Vita. Description based on print version record. Includes bibliographical references (leaves 118-119).

Globalisation has come as a double-edged blade for information technology (IT) companies; providing growth opportunities and yet posing many challenges. Software development is moving from a monolithic model to a distributed approach, where many entities and organisations are involved in the development process. Risk management an important area to deal with all the kinds of technical and social issues within companies planning and programming schedules, and this new way of working requires more attention to be paid to the temporal, socio-cultural and control aspects than before. Multinational companies like IBM have begun to consider how to address the distributed nature of its projects across the globe. With outlets across the globe, the company finds various people of different cultures, languages and ethics working on a single and bigger IT projects from different locations. Other IT companies are facing the same problems, despite there being many kinds of approaches available to handle risk management in large scale IT companies. IBM commissioned the Distributed Risk Management Process (DRiMaP) model as a suitable solution. This model focused on the collaborative and on-going control aspects, and paid attention to the need for risk managers, project managers and management to include risk management into all phases of projects and the business cycle. The authors of the DRiMaP model did not subject it to extensive testing. This research sets out to evaluate, improve and extend the model process and thereby develop a new and dynamic approach to distributed information systems development. To do this, this research compares and contrasts the model with other risk management approaches. An Evolutionary Model is developed, and this is subjected to empirical testing through a hybrid constructive research approach. A survey is used to draw out the observations of project participants, a structured interview gathered the opinions of project experts, a software tool was developed to implement the model, and SysML and Monte Carlo methods were applied to this to simulate the functioning of the model. The Evolutionary Model was found to partially address the shortcomings of the DRiMaP model, and to provide a valuable platform for the development of an enterprise risk management solution.

The number of statistical and mathematical credit risk models that financial institutions use and manage due to international and domestic regulatory pressures in recent years has steadily increased. This thesis examines the evolution of model risk management and provides some guidance on how to effectively build and manage different bagging and boosting machine learning techniques for estimating expected credit losses. It examines the pros and cons of these machine learning models and benchmarks them against more conventional models used in practice. It also examines methods for improving their interpretability in order to gain comfort and acceptance from auditors and regulators. To the best of this author’s knowledge, there are no academic publications which review, compare, and provide effective model risk management guidance on these machine learning techniques with the purpose of estimating expected credit losses. This thesis is intended for academics, practitioners, auditors, and regulators working in the model risk management and expected credit loss forecasting space. / Dissertation / Doctor of Philosophy (PhD)

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