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New Stanford center blends engineering with finance for better risk modeling

Faculty to use data analytics to provide faster, more accurate risk modeling that could help prevent future financial meltdowns.
An illustration of the remarkable interconnectedness in the U.S. banking industry. Blue dots represent the biggest banks. Smaller banks are arranged according to their financial exposure and, therefore, risk to default by the larger institutions. 

Sobered by the financial crisis of 2008 and inspired by the growing ability to crunch massive amounts of data, Stanford University engineers have founded the Center for Financial and Risk Analytics to harness advances in data analytics to evaluate the global financial system in real time.

“The international financial system generates terabytes of data every day. With the advent of smarter algorithms and increased computational power, we can now scour such vast stores of data to assess risk,” said Kay Giesecke, an associate professor of management science and engineering, who will direct the center.

The Center for Financial and Risk Analytics will pioneer models, algorithms and numerical tools required to process massive amounts of data and shape eventual applications that could find use in other data-rich contexts such as social networks. These tools and models will help the financial industry and regulators get a clearer picture of the true state of the system at any given moment.

Today’s market data sets contain massive amounts of fine-grained data collected during long periods of time. These stores of data in turn feed the financial industry’s predictive models and risk forecasting and drive trading decisions throughout the day. Meanwhile, regulators are trying to keep up with the industry, driving a need for deeper and more transparent data analysis.

While data is important, another aspect of financial systems has been less explored – networks. In many ways, the financial system is a vast interconnected network (see graphic that accompanies this story) made up of organizations such as banks, hedge funds, insurance companies and the like.

“Understanding how these networks are created, how they are connected and how they function could prove as fruitful as our deeper understanding of the data,” Giesecke said.

The team will include Giesecke and professors Peter Glynn, Ashish Goel, Ramesh Johari, Ben Van Roy and Yinyu Ye from Management Science and Engineering, and Stephen Boyd from Electrical Engineering. All of of the team members have significant research and teaching interests in fields such as financial engineering, stochastics, optimization, big data, networks and algorithms.

“Though finance and engineering might at first seem disparate, only engineers have the technical skills to develop new ways of looking at data and the acumen to pull it off,” Giesecke said.

Additionally, the new center will develop partnerships with public and private organizations, including financial technology companies, financial services firms, organizations such as the Office of Financial Research at the U.S. Treasury Department and the Office of the Comptroller of the Currency.

Joining engineering know-how in data analytics with industry and regulatory insights, Giesecke said, is a powerful combination that will take financial data analytics into a new era of bigger, faster and more accurate financial risk modeling.

“And this will help the entire industry – financial institutions and regulators alike – make better and more informed decisions that could help prevent another episode like we experienced in 2008,” Giesecke said.