Undergraduate Courses 2017-18

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 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.

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.

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.