Undergraduate Courses 2016-17

Risk exists in every aspect of our life. Risk management is a concept used in many disciplines. This subject provides an opportunity for students to examine the various kinds of risk existing in society and learn about the benefits and impacts of risk management. The focus is on the definition and classification of risk management, and its impact. The course focuses on risks in enterprise business, financial industry and our environment. Learning activities consist of a mixture of lectures, case studies, group discussion and seminars.

Business intelligence is a new discipline that comprises a set of theories, methodologies and processes to analyze business data with the aim of improving an organization’s decision making, business planning and projection. The aim of this course is to introduce various types of business intelligence and discuss the effects of various social problems on the use of business intelligence. The course also focuses on an analysis of the strengths, limitations, causes and impacts of the use of business intelligence. The topics include business intelligence strategies, the power of social influence, data collection and management, confidentiality and privacy issues, business ethics, and organizational and human behavioral changes. Also included are case studies of real social problems involving the use of business intelligence and group discussion of various social issues.

This course is designed to provide academic advising to students throughout their studies in the RMBI program, to enhance their understanding of the industries relevant to Risk Management and Business Intelligence and the related techniques expected by the market. Students are required to attend seminars and discussion sessions. For RMBI students only. Graded P/PP/F.

This course presents basic concepts and techniques for risk management and business intelligence. Various types of risk such as market risk, credit risk and operational risk are discussed with business applications and regulatory issues. Modern development of business intelligence, data management techniques and related applications like financial analysis, risk assessment, customer relationship management and human capital productivity analysis are also presented.

This subject provides an opportunity for students to learn about the current development in the field of risk management and business intelligence. A range of professional activities are eligible for study, including seminars/ lectures, professional activities/ meetings, journal readings or structured readings.

This course focuses on the products and processes in the financial industries. It analyzes and evaluates the designs and performances of the internal operations and the different distribution channels of the financial institutions, and identifies opportunities for continuous improvement in productivity and efficiency. It also covers the issues of quality control and operational risk management, the application of IT in the industry. It will also discuss the automation/outsourcing of the financial operations for non-financial institutions to improve the financial performance and risk management in the supply chain.

The course covers statistical analysis, simulation and optimization methods that are commonly used in management of both market and credit risk. Risk factors and loss distribution. Losses over several periods and scaling. Coherent measures of risk. Capital allocation. Extreme value analysis. Importance sampling. Simulation techniques, including random number generations, variance reduction methods and statistical analysis of simulation outputs. Bernoulli mixture models. Financial and actuarial pricing of credit risk. Case studies of major credit losses.

The course covers the theoretical basis of actuarial models and their applications to insurance risk. The topics discussed in this course include survival models, life tables and selection, insurance benefits, annuities, premium calculation, insurance policy values, multiple state models, pension plan valuation, interest rate risk in insurance, and emerging costs for life insurance.

This course will explore some advanced principles and techniques of data mining, with emphasis on applications in risk management and business intelligence. Topics include data mining process for data transformation and integration, data preprocessing, data mining algorithms and evaluation of data mining models and results. Advanced topics include data stream analysis, using data warehouse for decision support, supervised, semi-supervised and unsupervised learning techniques in data mining. We will cover advanced data mining applications in credit risk analysis, scale up methods for mining massive customer data and various novel applications such as data mining applications in social network analysis. Projects are aimed at familiarize the students with the entire data mining process rather than isolated applications.

Student conducts an in-depth study on selected topics in Risk Management and Business Intelligence under the supervision of a faculty. Scope may include the identification of a non-reference problem, followed by subsequent analysis and solution of the problem. Creative and critical thinking in the decision processes made under uncertainty are expected to be demonstrated in the project.

This is the continuation of RMBI 4980. Student conducts an in-depth study on selected topics in Risk Management and Business Intelligence under the supervision of a faculty. Scope may include the identification of a non-reference problem, followed by subsequent analysis and solution of the problem. Creative and critical thinking in the decision processes made under uncertainty are expected to be demonstrated in the project.