INTRODUCTION TO FINANCE
This elective is available to and highly recommended for students without a strong finance background. It introduces students to the fundamental financial issues of the modern corporation. By the end of this course, students will understand the basic concepts of financial planning, growth management, debt financing, equity valuation, and capital budgeting. (This course is not automatically available for all students; students must contact their Advisor to determine eligibility to register.)
- T 8:10 p.m.-10 p.m.
- M 6:10 p.m.-8 p.m.
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Instructor:
Christakis Droussiotis
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Instructor:
Seydina Fall
Info - 3 points
MANAGING HUMAN BEHAVIOR IN THE ORGANIZAT
MNG HUMAN BEHAVIOR IN THE
In this course, students will gain an overview of major concepts of management and organization theory, concentrating on understanding human behavior in organizational contexts, with a heavy emphasis on the application of concepts to solve managerial problems. Students will work in a combination of conceptual and experiential activities, including case studies, discussions, lectures, simulations, videos, and small group exercises.
By the end of this course students will:
Develop the skills to motivate employees
Establish professional interpersonal relationships
Take a leadership role
Conduct performance appraisals
- W 8:10 p.m.-10 p.m.
- T 6:10 p.m.-8 p.m.
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Instructor:
Raymond Lamanna
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Instructor:
Patti Ippoliti
Info - 3 points
VALUE-BASED ENTERPRISE RISK MANAGEMENT
VALUE-BASED ERM
Foundational ERM course. Addresses all major ERM activities: risk framework; risk governance; risk identification; risk quantification; risk decision making; and risk messaging. Introduces an advanced yet practical ERM approach based on the integration of ERM and value-based management that supports integration of ERM into decision making. Provides a context to understand the differences between (a) value-based ERM; (b) traditional ERM; and (c) traditional "silo" risk management.
- M 6:10 p.m.-8 p.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
Duncan Campbell
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Instructor:
David A. Bartholomew
Info - 3 points
TRADITIONAL ERM PRACTICES
This course provides an overview of the traditional ERM frameworks used to identify, assess, manage, and disclose key organizational risks. The traditional ERM frameworks are those that are more commonly in use and include COSO ERM, ISO 31000, and the Basel Accords. This course also provides an understanding of the methods, tools, techniques, and terminology most organizations use to manage their key risks, presented in the context of the foundational elements of an ERM process. This will enable students to navigate the ERM landscape within most organizations, and, along with the second-semester course Value-Based ERM, evaluate opportunities to enhance the existing ERM practices and evolve their ERM programs over time.
- M 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
- R 8:10 p.m.-10 p.m.
- M 8:10 p.m.-10 p.m.
- T 6:10 p.m.-8 p.m.
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Instructor:
Rajeev Dave
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Instructor:
H.S. Bob Kostakopoulos
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Instructor:
Marianna Belyavskiy
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Instructor:
Peter Bruzzese
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Instructor:
Mary Ellen Luning
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Instructor:
James S Kim
Info - 3 points
EXTERNAL STAKEHOLDER REQUIREMENTS
Provides a global review of ERM requirements of regulators, rating agencies, and shareholders. Addresses three industry sectors: (1) insurance; (2) banking; and (3) corporate.
- W 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
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Instructor:
Chris Lee
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Instructor:
R S Forston
Info - 3 points
STRATEGIC COMMUNICATIONS FOR RISK PROFES
STRATEGIC COMM FOR RISK P
Workshop-like course that addresses a variety of communication skills, including listening skills, presentation skills, leadership communications, conflict resolution, management interactions, and professional communication techniques.
- T 2:10 p.m.-4 p.m.
- M 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
- T 6:10 p.m.-8 p.m.
- R 8:10 p.m.-10 p.m.
- M 4:10 p.m.-6 p.m.
- T 6:10 p.m.-8 p.m.
- M 6:10 p.m.-8 p.m.
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Instructor:
Sim Segal
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Instructor:
Alan Cheung
h-index: 5 Info -
Instructor:
Mark Jankiewicz
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Instructor:
Alison L Bird
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Instructor:
Harish Dakshina
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Instructor:
Violet Diamant
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Instructor:
Joseph Dziwura
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Instructor:
Daniel D Walker
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Instructor:
Beth Goldberg
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Instructor:
Neil Barman
Info - 3 points
STRATEGIC RISK MANAGEMENT
Review of the types of strategic risks, such as a flawed strategy, inability to execute the strategy, competitor risk, supply chain risk, governance risk, regulatory risk, M&A risk, international risk, etc. Includes case studies, research, and common mitigation techniques, such as strategic planning practices, management techniques, governance practices, supply-chain management, etc.
- W 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
- M 4:10 p.m.-6 p.m.
- M 8:10 p.m.-10 p.m.
- R 8:10 p.m.-10 p.m.
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Instructor:
Raj Mittal
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Instructor:
Celia Kapsomera
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Instructor:
Yvonne Pytlik
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Instructor:
Fenton Aylmer
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Instructor:
Carlo Di Florio
Info - 3 points
OPERATIONAL RISK MANAGEMENT
Review of the types of operational risks, such as technology risk (e.g., cyber-security), human resources risk, disasters, etc. Includes case studies, risk analysis frameworks and metrics, and common mitigation techniques, such as insurance, IT mitigation, business continuing planning, etc.
- M 6:10 p.m.-8 p.m.
- R 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
- R 6:10 p.m.-8 p.m.
- W 6:10 p.m.-8 p.m.
- R 8:10 p.m.-10 p.m.
- W 8:10 p.m.-10 p.m.
- R 8:10 p.m.-10 p.m.
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Instructor:
Rossano Rossi
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Instructor:
Marianna Belyavskiy
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Instructor:
Penny Cagan
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Instructor:
Raj Mittal
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Instructor:
Francis Tobin
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Instructor:
Sherry L Stevens
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Instructor:
Lisa Broomer
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Instructor:
Saba Zahid
Info - 3 points
INTRO TO QUANTITATIVE RISK MANAGEMENT
INTRO TO QUANT RISK MGMT
Students without a strong math background will require significant additional time and effort to achieve the learning objectives and work through the course assignments. This course builds a foundation in the mathematics and statistics of risk management. Students are empowered to understand the output of quantitative analysts and to do their own analytics. Concepts are presented in Excel and students will have the opportunity to practice those concepts in Excel, R or Python. This course is a required prerequisite for registering for the following courses: Financial Risk Management, Insurance Risk Management, ERM Modeling.
- M 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
- M 8:10 p.m.-10 p.m.
- T 4:10 p.m.-6 p.m.
- T 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
- R 6:10 p.m.-8 p.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
David Weltz
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Instructor:
Petr Chovanec
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Instructor:
Witold Czubala
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Instructor:
Fabrice Fiol
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Instructor:
Oleksandra V Lyulko
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Instructor:
Arun B Venkatarangan
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Instructor:
Eric Tam
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Instructor:
Michael Ding
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Instructor:
Sudeep Lahiri
Info - 3 points
CODING FOR RISK MANAGEMENT
Equips students with the ability to adopt the programming culture typically present in the ERM/risk areas of most financial organizations. By studying Python, SQL, R, git, and AWS, students gain exposure to different syntaxes. Students apply these skills by coding up market risk and credit risk models. Students also gain familiarity with working in the cloud.
- T 8:10 p.m.-10 p.m.
- R 8:10 p.m.-10 p.m.
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Instructor:
Zaur Nurtazin
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Instructor:
David J Romoff
Info - 3 points
FINANCIAL RISK MANAGEMENT
A survey of market, credit, liquidity, and systemic risk. Includes case studies, risk quantification methods, and common mitigation techniques using portfolio management, hedging, and derivatives. Also addresses traditional risk management practices at banking institutions.
- W 8:10 p.m.-10 p.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
Amit Matta
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Instructor:
Justin Hon
Info - 3 points
FRM PRACTICES
Provides the opportunity to learn how business units operate at an investment bank. Several industry practitioners each spend one to two sessions providing a hands-on experience that recreates the operations and decision-making of front, middle, and back offices work at a bank. Students typically learn the common activities, the data inputs, the analytics, and the applications of the insights.
- W 6:10 p.m.-8 p.m.
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Instructor:
Qingji Yang
Info - 3 points
QUANTITATIVE RISK MANAGEMENT
Quantitative Risk Management continues building your quantitative foundation in order to work with more advanced models and use mathematical and statistical intuition for building those models. At the end of this course, you will be able to use analytics algorithms for risk management; use factor models to assess the quality of investment portfolios and trader positions; hedge equity, option, and fixed-income portfolios using derivatives; estimate volatility with options models and GARCH models; and model ESG and Climate risk.
The course is highly structured and organized by topic into semester long learning threads. Each week, readings and assignments will take another step forward along these threads: regression models, classification models, time series analysis, options and volatility modeling, fixed income modeling, factor models and portfolio management, tail risk modeling. These concepts will be demonstrated in python and students are expected to be able to understand and run python code.
- W 6:10 p.m.-8 p.m.
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Instructor:
Christopher Mann
Info - 3 points
INSURANCE RISK MANAGEMENT
Review of types of insurance risk, such as pricing risk, underwriting risk, reserving risk, etc. Includes case studies, risk quantification methods (e.g., market-consistent economic capital models, dynamic financial analysis (DFA) models, catastrophe models, etc.), and common mitigation techniques, such as asset-liability management (ALM), reinsurance, etc. Also addresses traditional risk management at insurance companies and ERM actuarial standards of practice (ASOPs).
- M 8:10 p.m.-10 p.m.
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Instructor:
Melissa Miller
Info - 3 points
CREDIT RISK MANAGEMENT
Credit Risk Management requires business acumen, the monitoring of internal and external data, disciplined execution, and organizational intelligence. A solid understanding of this enables a credit risk manager to help organizations achieve their objectives. Through readings, case studies, and modeling projects, students learn how risk managers decide on credit risk management strategy applied throughout the client lifecycle.
- R 8:10 p.m.-10 p.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
Rahmi E Aktug
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Instructor:
Weijun Weng
Info - 3 points
MARKET RISK MANAGEMENT
This course provides the tools to measure and manage market risk in the context of large financial institutions. The volume and complexity of the data itself, at large institutions, makes it a challenge to generate actionable information. We will take on this challenge to master the path from data to decisions.
We cover the essential inputs to the engines of financial risk management: VaR, Expected Exposure, Potential Exposure, Expected Shortfall, backtesting, and stress testing as they apply to asset management and trading. We explore the strengths and weaknesses of these different metrics and the tradeoffs between them. We also cover how regulatory frameworks impact both the details and the strategy of building these engines. Lastly, we cover counterparty-credit methodologies, mainly as they apply to Trading Book risk.
- R 6:10 p.m.-8 p.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
Igor Axenov
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Instructor:
Deniz Ozenbas
Info - 3 points
CREDIT RISK ANALYTICS
The field of credit risk management is undergoing a quiet revolution as subjective and manually-intensive methods give way to digitization, algorithmic management, and decision-making. This course provides a practical overview and hands-on experience with different methods, and it also provides a view of future technologies and discussions of potential future directions. Participants in this course should be well-positioned to take entry-level analytic positions and help drive strategic decisions.
The first half of the course explores analytics used today for credit risk management. You will learn to create rating and scoring models and a macro scenario-based stress testing model. In the second half of the course, we explore more advanced tools used by the more prominent organizations and fintech firms, including neural net and XGBoost decision tree models.
- W 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
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Instructor:
Douglas W Dwyer
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Instructor:
Sudeep Arora
Info - 3 points
LIQUIDITY RISK MANAGEMENT
Indicators of companies running into hard times typically include revenue volatility, loss of key personnel, reputational damage, and increased litigation. However, company failures are frequently marked by insufficient liquidity, or the lack of cash to meet obligations. Liquidity risk is the unexpected change in a company’s cash resources or demands on such resources that results in the untimely sale of assets, and/or an inability to meet contractual demands and/or default. In extreme cases, the lack of sufficient cash creates severe losses and results in company bankruptcy.
An institution’s cash resources and obligations can and must be managed. Indeed, the field of liquidity risk management is an established part of treasury departments at sizable institutions. The regularity of cash flows and the turbulence of business and markets must be assessed and quantified. This course provides students the tools and techniques to manage all types of liquidity challenges including the need to sell assets unexpectedly in the market, or work through ‘‘run‐on-the‐bank’’ situations for financial services companies.
- T 8:10 p.m.-10 p.m.
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Instructor:
Joseph Dziwura
Info - 3 points
BLOCKCHAIN AND ERM
Using Blockchain, decisions can be made without relying on a single centralized authority, allowing for greater transparency and trust between participants. By using smart contracts and distributed ledgers, users can easily create, modify, and manage agreements between stakeholders, ensuring that all parties have access to the same information and can make informed decisions. As a result, Blockchain technology reduces the risks associated with decision-making, and improves efficiency and accuracy. This course first examines the risks and rewards of implementing Blockchain at large organizations engaging in decentralized decision-making processes. The course then explores the Blockchain as a tool for risk management.
- W 8:10 p.m.-10 p.m.
- R 8:10 p.m.-10 p.m.
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Instructor:
Ivan Bakrac
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Instructor:
Claire Husson-Citanna
Info - 3 points
APPLIED CODING FOR RISK MANAGEMENT
Applied Coding for Risk Management takes your ability to use code to the next level. This course is for students who have taken Coding for Risk Management. The goal of that course is to give you the ability to get risk management related work done with code. The goal of this course is to give you the ability to code in collaboration with teams in order to build and deploy risk models on large data sets. The course is highly structured and organized by topic into semester long learning threads. Each week, readings and assignments will take another step forward along these threads: Python and collaborative coding, SQL for business intelligence, data analysis and visualization, cloud-based infrastructure, simulation for risk management, financial data processing, and API’s.
- T 8:10 p.m.-10 p.m.
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Instructor:
David J Romoff
Info - 3 points
PRIVACY RISK MANAGEMENT
In today’s digital age, with the collection and usage of personal information growing at an exponential rate, the study of privacy risk management is crucial. As organizations grapple with the dual challenge of monetizing technological innovation without running afoul of regulatory and legal restrictions, the ERM professional who understands how to identify, assess, and manage privacy risk is in high demand. In this course, students will develop an understanding of the legal frameworks governing data usage, the ethical issues associated with the use of personal information, and how to develop robust privacy frameworks and controls in order to manage privacy risk.
- T 8:10 p.m.-10 p.m.
- R 6:10 p.m.-8 p.m.
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Instructor:
Alison L Bird
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Instructor:
Erin Bansal
Info - 3 points
COMPANY FAILURES
Examination of areas critical to an organization’s success from strategic, operational, financial, and insurance perspectives, and examines why many companies fail in spite of the vast knowledge of factors driving success. Several case studies examined in depth.
- M 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
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Instructor:
Adam H Litke
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Instructor:
Michael Zwecher
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Instructor:
Steve Lindo
Info - 3 points
RISK REGULATIONS IN FINANCE
This course examines post-financial crisis regulations including Basel III, Fundamental Review of the Trading Book (FRTB), Dodd-Frank Act, Supervision and Regulation Letter 11-7 (SR 11-7), and others. Case studies will explore the technical details of these new rules; and guest lectures from industry experts will bring the material to life. Areas of focus include: model risk management, stress testing, derivatives, and insurance. By the end of this course students will be able to:
Evaluate the purpose and limitations of risk regulations in finance.
Identify and communicate weaknesses in a financial firm.
Communicate with regulators.
Understand Recovery and Resolution Plans or “Living Wills” for a financial firm.
- R 6:10 p.m.-8 p.m.
- W 6:10 p.m.-8 p.m.
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Instructor:
Jean Michel Ngalikpima
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Instructor:
Juan Echeverri
Info - 3 points
ESG AND ERM
ESG will be a driving force in risk management in upcoming years. ERM / Risk professionals need a solid understanding of emerging ESG trends and regulations and how they apply to day-to-day job responsibilities. The ESG and ERM course begins with an overview of the ESG landscape and framework. After a foundational understanding is established, the course focuses on incorporating ESG into enterprise risk management, including identification, quantification, decision making, and reporting of ESG-related risks.
- M 8:10 p.m.-10 p.m.
- T 8:10 p.m.-10 p.m.
- W 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
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Instructor:
Rajat Baijal
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Instructor:
Jean Michel Ngalikpima
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Instructor:
Helda Rock
Info - 3 points
DERIVATIVES RISK MANAGEMENT
This course explores financial derivatives across different asset classes with in-depth analysis of several popular trades including block trades, program trades, vanilla options, digital options, and variance swaps. Their dynamics and risks are explored through Monte Carlo simulation using Excel and Python. The daily decisions and tasks of a frontline risk manager are recreated and students have the opportunity to see which trades they would approve or reject. Students will gain a working knowledge of financial derivatives and acquire technical skills to answer complex questions on the trading floor.
- T 6:10 p.m.-8 p.m.
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Instructor:
Alberto Scalari
Info - 3 points
TOOLS FOR RISK MANAGEMENT
Tools for Risk Management examines how risk technology platforms assess risks. These platforms gather, store, and analyze data; and transform that data to actionable information. This course explores how the platforms are implemented, customized, and evaluated. Topics include business requirements specification, data modeling, risk analytics and reporting, systems integration, regulatory issues, visualization, and change processes. Hands-on exercises using selected vendor tools will give students the opportunity to see what these tools can offer.
- W 6:10 p.m.-8 p.m.
- R 8:10 p.m.-10 p.m.
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Instructor:
Barney Baldwin
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Instructor:
Viktor Ziskin
Info - 3 points
ERM MODELING
Equips students with the basics of risk measurement and simulation using a hands-on approach to ERM modeling. Using industry-standard simulation software, students build systems of risk drivers for finance and insurance companies. Topics include risk correlations, VaR and TVaR, capital modeling, capital allocation, and parameter, process, and model Risk. Students acquire both quantitative experience building models and qualitative appreciation for model weaknesses.
- W 8:10 p.m.-10 p.m.
- M 6:10 p.m.-8 p.m.
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Instructor:
Julian Horky
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Instructor:
Tom Passante
Info - 3 points
MACHINE LEARNING FOR RISK MANAGEMENT
The exponentially increasing availability of data and the rapid development of information technology and computing power have inevitably made Machine Learning part of the risk manager’s toolkit. But, what are these tools? This class provides the driving intuitions for machine learning. Students will see how many of the algorithms are extensions of what we already do with our human minds. These algorithms include regularized regression, cluster analysis, naive bayes, apriori algorithm, decision trees, random forests, and boosted ensembles.
Through practical and real-life applications of ML to Risk Management, students will learn to identify the best technique to apply to a particular risk management problem, from credit risk measurement, fraud detection, portfolio selection to climate change, and ESG applications.
- M 6:10 p.m.-8 p.m.
- 3 points
INFORMATION TECHNOLOGY RISK MANAGEMENT
IT RISK MANAGEMENT
Students will learn how to better identify and manage a wide range of IT risks as well as better inform IT investment decisions that support the business strategy. Students will develop an instinct for where to look for technological risks, and how IT risks may be contributing factors toward key business risks. This course includes a review of IT risks, including those related to governance, general controls, compliance, cybersecurity, data privacy, and project management. Students will learn how to use a risk-based approach to identify and mitigate cybersecurity and privacy related risks and vulnerabilities. No prior experience or technical skills required to successfully complete this course.
- T 6:10 p.m.-8 p.m.
- M 6:10 p.m.-8 p.m.
- W 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
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Instructor:
Shahryar Shaghaghi
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Instructor:
Kiran Bhujle
Info - 3 points