Undergraduate Courses 2025-26

Undergraduate courses marked with [BLD] or [SPO] may be offered in the mode of blended learning or self-paced online delivery respectively, subject to different offerings. Students should check the delivery mode of the class section before registration.

• RMBI 1010

  Risk Management in Financial Institutions

  3 Credit(s)
  Description

  Risk exists in every aspect of our daily life, and risk management is essential for any organization to achieve their business objectives. This course 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 course focuses on risk measurement and analysis in enterprise business and modern financial institutions. Learning activities consist of a mixture of lectures, case studies and lab exercises. From taking this course, students’ awareness of risk management will be raised. They can also improve their critical thinking and analytical skills, and become responsible and ethical citizens.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Define and classify various kinds of risks in financial institutions
  • 2.
    Discuss the reasons and benefits of risk management
  • 3.
    Articulate the general risk management framework in a business setting
  • 4.
    Examine various risk measurement techniques and practice in the industry
  • 5.
    Apply a certain level of quantitative risk analysis in various business scenarios
• RMBI 1020

  Business Intelligence for Data-Driven Decisions

  3 Credit(s)
  Mode of Delivery

  [BLD] Blended learning

  Description

  Business intelligence is a 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. Tomorrow’s employees and executives may not need the advanced skills of a data scientist, but they must be data literate. Therefore, there is a need to bring together data skills and business management by introducing a common core course, specializing at applying data analytics techniques to business scenarios for the implementation of business intelligence. The aim of this course is to introduce the general workflow of business intelligence and various applications of data analytics models in business scenarios. The course also focuses on an analysis of the strengths, limitations, causes and benefits of the use of business intelligence. The course also includes hands-on practical exercises by using data mining and statistics models on real business data sets.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Outline and describe different types of business intelligence methods
  • 2.
    Examine and describe how business intelligence methods can facilitate business decision making and improve the profitability of a company
  • 3.
    Articulate the difficulties and complexities of managing BI projects, as well as the social and economic impact of such applications
  • 4.
    Apply data analytics techniques on small datasets for a simple implementation of business intelligence
• RMBI 2000

  Special Topics in Risk Management and Business Intelligence

  0-4 Credit(s)
  Description

  This special topics course provides an opportunity for students to understand the fundamental knowledge and 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, structured readings, discussion sessions etc depending on the nature of topics. The course maybe repeated for credits if different topics are taken. Enrollment may require approval of the course instructor. May be graded by letter, P/F or DI/PA/F for different offerings.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Equip with broad and useful knowledge to various topics which are not covered by existing courses
  • 2.
    Understand the importance of the fundamental knowledge required for learning the RM / Bl related techniques
  • 3.
    Better plan their studies and future career path
  • 4.
    (Each offering under the umbrella will have specific learning outcomes)
• RMBI 2001

  Academic and Professional Development in Risk Management and Business Intelligence

  0 Credit(s)
  Description

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Plan their studies according to the future career they pursue
  • 2.
    Recognize the relevant industries of Risk Management and Business Intelligence and the future development
  • 3.
    Understand the importance of the fundamental knowledge required for learning the techniques relevant to RM and BI
• RMBI 3000

  Special Topics in Risk Management and Business Intelligence

  0-4 Credit(s)
  Description

  This special topics course provides an opportunity for students to gain a more in-depth knowledge required for learning the techniques relevant to RM and BI. A range of professional activities are eligible for study, including seminars/ lectures, professional activities/ meetings, journal readings, structured readings, discussion sessions etc depending on the nature of topics. The course maybe repeated for credits if different topics are taken. Enrollment may require approval of the course instructor. May be graded by letter, P/F or DI/PA/F for different offerings.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Equip with broad and useful knowledge to various topics which are not covered by existing courses
  • 2.
    Gain a more in-depth knowledge required for learning the techniques relevant to RM and Bl
  • 3.
    Better plan their studies and future career path
  • 4.
    (Each offering under the umbrella will have specific learning outcomes)
• RMBI 3010

  Data Analytics with R

  2 Credit(s)
  Prerequisite(s)

  RMBI 3110

  Exclusion(s)

  RMBI 4000E, ISOM 3390

  Description

  This course provides a hands-on introduction to R as a programming language and environment for data analytics and visualization. It will cover the basic syntax including functions and flow control, some commonly used data structures such as vectors, lists, matrices and data frames, as well as data importing and visualization in R. The course will also introduce a few primary data cleaning techniques in dealing with missing values, duplicates and inconsistency, and means to implement simple data transformation and normalization with R. Classic data mining models and the corresponding packages in R will also be presented, with the focus on model fine tuning and parameter calibration for practical applications. For RMBI students only.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Demonstrate competency in the use of data structures and functions in R
  • 2.
    Test, trace and debug small programs written in R
  • 3.
    Apply basic exploratory techniques and classic data mining models in R to real data analysis problems
• RMBI 3020

  Practicing Risk Management using Case Studies

  3 Credit(s)
  Description

  This course will examine various types of risks that many business enterprises face nowadays; for example, financial markets risks can be further divided into equity risk, foreign exchange risk, credit risk, interest rate risk and commodity risk. In addition, other non-financial risk factors such as operational risk, reputational risk, catastrophe risk (earthquake/typhoon), geopolitical risk, cybersecurity risk, counterparty risk and liquidity risk will also be discussed. The format of the course is based on a series of real-life case studies from well-known companies (Alibaba, GM, Enron, Citibank, Merrill Lynch etc.) and educational institutions (Harvard & Yale etc.) Students must prepare for every class by having read the assigned cases before arriving class. Participation in every case discussion is mandatory.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Acquire domain knowledge on assessing financial risk and non-financial risk
  • 2.
    Learn to apply qualitative and quantitative risk analysis skills in various business cases
  • 3.
    Develop effective communication skills between teams and individuals with opposing views under the risk management context
  • 4.
    Enhance problem solving skills and decision-making skills for business scenarios with uncertainty and limited sources by risk mitigation planning
  • 5.
    Make legal and business arguments based on facts and logical interpretations of precedent successful/failed risk management cases
• RMBI 3110

  Introduction to Risk Management and Business Intelligence

  3 Credit(s)
  Prerequisite(s)

  ISOM 2500 or MATH 2411

  Description

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Apply and evaluate quantitative risk analysis tools to make smart decision in financial markets.
  • 2.
    Interpret and apply the business intelligence technique to make smart decision in financial markets. .
• RMBI 4000

  Special Topics in Risk Management and Business Intelligence

  1-4 Credit(s)
  Description

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Describe key milestones and emerging issues in the fields of risk management and business intelligence.
• RMBI 4210

  Quantitative Methods for Risk Management

  3 Credit(s)
  Prerequisite(s)

  ISOM 3540 OR MATH 2421 OR MATH 2431

  Description

  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.
• RMBI 4220

  Life Contingencies Models and Insurance Risk

  3 Credit(s)
  Alternate code(s)

  MATH 4513

  Prerequisite(s)

  ELEC 2600 OR ISOM 3540 OR MATH 2421 OR MATH 2431

  Description

  The topics discussed in this course include survival models, life tables and selection, insurance benefits, annuities, premium calculation, and insurance policy values.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand the models used to model decrements used in insurances, annuities, and investments and how to calculate probabilities based on those models
  • 2.
    Understand the models used to model cash flows of life insurances and annuities and how to calculate the present values of the cash flows based on those models
  • 3.
    Understand the methods for calculating benefit reserves for traditional life insurances and annuities and basic universal life insurances
  • 4.
    Understand the models used to model cash flows for basic universal life insurances and how to calculate present values and contract level values
  • 5.
    Understand the relationship between expenses and gross premium and how to calculate contract level values based on the gross premium for life insurances and annuities
  • 6.
    Enhance the ability of using advanced mathematics (calculus, probability, statistics, etc.) to solve real-world problems
  • 7.
    Enhance the ability of building stochastic and statistical models for life contingencies
  • 8.
    Enhance the ability of using computational software such as Matlab and implementing mathematical models using computer programs
• RMBI 4310

  Advanced Data Mining for Risk Management and Business Intelligence

  3 Credit(s)
  Co-list with

  COMP 4332

  Prerequisite(s)

  COMP 4331 or ISOM 3360

  Description

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Understand issues related to real-world data mining.
  • 2.
    Demonstarte an ability to master the tools and skills for large-scale data mining projects.
  • 3.
    Describe the key concepts and applications of business intelligence and social media analytics.
• RMBI 4980

  Risk Management and Business Intelligence Capstone Project I

  4 Credit(s)
  Prerequisite(s)

  COMP 4331 OR ISOM 3360

  Description

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Write a proposal for a company project.
  • 2.
    Suggest a scope for a company research problem.
  • 3.
    Identify suitable risk management and business intelligence techniques to solve the research problem.
  • 4.
    Decide the timeline of the project.
  • 5.
    Present and deliver all outcomes of the project.
• RMBI 4990

  Risk Management and Business Intelligence Capstone Project II

  4 Credit(s)
  Prerequisite(s)

  RMBI 4980

  Description

  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.

  Intended Learning Outcomes

  On successful completion of the course, students will be able to:

  • 1.
    Write a proposal for a company project.
  • 2.
    Suggest a scope for a company research problem.
  • 3.
    Identify suitable risk management and business intelligence techniques to solve the research problem.
  • 4.
    Decide the timeline of the project.
  • 5.
    Present and deliver all outcomes of the project.