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.
• Instructor: David J Romoff
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• 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.
• M 6:10 p.m.-8 p.m.
• W 8:10 p.m.-10 p.m.
• Instructor: Shahryar Shaghaghi
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• Instructor: Kiran Bhujle
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• 3 points

THIRD-PARTY RISK MANAGEMENT

As organizations increasingly rely on external vendors and service providers, managing third-party risks becomes paramount to ensure operational resilience, regulatory compliance, and strategic success. Challenges include:


The evolving nature of technology risks.

The impact of geopolitical tensions.

The lessons learned from disruptive events like pandemics.


By offering a comprehensive curriculum covering everything from the basics of vendor management to advanced predictive TPRM models and emphasizing regulatory requirements specific to the financial services sector, the course equips professionals with the knowledge and tools needed to navigate the intricate web of third-party relationships. 
Students taking this course are prohibited from taking Supply Chain Risk Management for Non-Financials (ERMC PS5585) at any time. Contact your advisor for more information.

• R 6:10 p.m.-8 p.m.
• M 8:10 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: Shahryar Shaghaghi
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• 3 points

SUPPLY CHAIN RISK MANAGEMENT FOR NON-FIN

SUPPLY CHAIN RM FOR NON-F

The Pandemic made us all aware of the fragility of supply chains and how significant the consequences of failure of our supply chains can be. It is paramount to note that global and local economies can break down, and scarcity of essential resources can foment wars. Risk professionals must know what best practices bring security to supply chains and related companies, governments, and other institutions. 
Students taking this course are prohibited from taking Third-Party Risk Management (ERMC PS5575) at any time. Contact your advisor for more information.

• R 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• M 8:10 p.m.-10 p.m.
• Instructor: Daniel A Cahill
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• 3 points

COGNITIVE BIAS & ERM

Explores key concepts of behavioral economics and cognitive psychology, how to identify key cognitive biases in ERM activities, and how to apply techniques to address these, enhancing the quality and integrity of an ERM program. The course also includes best practices in leveraging analytic models to improve decision making.

• W 8:10 p.m.-10 p.m.
• R 8:10 p.m.-10 p.m.
• T 8:10 p.m.-10 p.m.
• Instructor: Rich Lauria
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• Instructor: Narae Lee
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• Instructor: Zava Aydemir
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• 3 points

ERM CAPSTONE

Teams will work through a case assignment, demonstrating mastery of key learnings gained throughout the program on an integrated basis. A simulated case study is used: this is a combination of publicly-available information of an actual company and simulated ERM program details, based on a blend of current ERM programs and practices in the marketplace. Each team will assess the case study and recommend enhancements.

• T 8:10 p.m.-10 p.m.
• R 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• W 6:10 p.m.-8 p.m.
• M 8:10 p.m.-10 p.m.
• T 6:10 p.m.-8 p.m.
• W 6:10 p.m.-8 p.m.
• T 8:10 p.m.-10 p.m.
• T 6:10 p.m.-8 p.m.
• Instructor: Rich Lauria
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• Instructor: Sam Gong
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• 3 points