About the course
The McKinsey Survey suggests the financial services industry has been one of the early adopters of AI technology. Just like other revolutionary technologies of the past, this presents new opportunities, from digital financial advisors to sophisticated automated detection of emotion and deception in trading. However, it also introduces new risks -- including but not limited to data leakage issues that may unfairly bias loan decisions, blackbox models that cannot be explained or audited, and novel ethical and legal concerns for regulators.
In order to minimize the economic and ethical impact of these risks, it is important to fully understand how machine learning integrates with banking, FinTech, and risk management in the financial sector. In this talk, we will dive deep into the current applications of AI/ML in financial services and explore the trade-off between promises vs. challenges.
This seminar is part of the Data Science and Machine Learning tracks of the Advanced Data Science Certificate.
The seminar will be held in an online format that is designed for interactive participation. During the presentation, a faculty panel will be available in text chat to answer general questions and to schedule questions for the AMA session.
During the AMA session, participants will be able to directly discuss the topics with the presenter in a symposium-like environment.
- 6:30-6:45 p.m. EDT - Introduction
- 6:45-7:30 p.m. EDT - Presentation
- 7:30-7:40 p.m. EDT - Intermission
- 7:40-8:20 p.m. EDT - AMA (“Ask me anything”)
- 8:20-8:30 p.m. EDT - Concluding Remarks
Upon successful completion of the seminar, students will be able to:
Identify the pre-conditions that have transformed artificial intelligence from a theoretical idea to a feasible, useful tool in industries like financial services.
Inspect current applications of AI/ML in the financial services industry.
Evaluate key trade-offs of increased automation in the finance sector, and begin to apply such cost-benefit analyses to other sectors.
Formulate a position about the responsibilities businesses, regulators, and data scientists have in producing and leveraging models that will not (unintentionally or otherwise) encode bias, threaten privacy, or unfairly advantage certain businesses or people.
With 20+ years of diverse experience in financial services, Andrew currently serves as a Principal Financial Analyst with a federal regulatory agency. Prior to that, he was a Fair Value Analysis Director with Freddie Mac responsible for fixed income portfolio valuation, risk and performance monit...
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Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.