Machine Learning in Financial Modeling
Student No.:100
Time:Tue 17:00, Mar.20
Instructor:Agus Sudjianto  
Place:Lecture Hall, Jin Chun Yuan West Bldg.
Starting Date:2018-3-20
Ending Date:2018-3-20

Speaker: Dr. Agus Sudjianto, Executive Vice President, Head of Corporate Model Risk, Wells Fargo Bank



Mathematical models are extensively used by financial institutions for various purposes such as to run the business or to fulfill regulatory requirements. Among the most critical usage of mathematical models is the evaluation of a bank’s financial health and its ability to sustain adverse economic scenarios. Mathematical models are also used to perform credit underwriting, portfolio management, derivative valuation and pricing, risk measurement and to prevent financial crime.


Historically, banks have employed traditional mathematical and statistical models. More recently, machine learning algorithms have gained strong adoption in both model development and validation, particularly due to their ability to deal with very large structured and unstructured data sets. In this talk, I will discuss the breadth of mathematical modeling applications in financial institutions, the role of machine and deep learning, and current challenges.


About the Speaker:

Agus Sudjianto is an executive  vice president and head of Corporate Model Risk for Wells Fargo, where he is responsible for enterprise model risk management and serves as the Chair of the Model Risk Committee.


Prior to his current position, he was the modeling and analytics director and chief model risk officer at Lloyds Banking Group in the United Kingdom.  Before joining Lloyds, he was a senior credit risk executive and head of Quantitative Risk at Bank of America.


Earlier in his career, Dr. Sudjianto held the position of product design manager in the Powertrain Division of Ford Motor Company.


Dr. Sudjianto holds several U.S. patents in finance and in engineering.  He has published numerous technical papers and is the co-author of the book: Design and Modeling for Computer Experiments. His technical expertise and interests include quantitative risk management, credit risk modeling, machine learning and computational statistics.


Dr. Sudjianto holds holds masters and doctorate degrees in engineering and management from Wayne State University and the Massachusetts Institute of Technology.