Data, Analytics, and Risk in Finance
Workshop on
8 Apr 2016
|
MIT Institute for Data, Systems, and Society

Amitabh Arora

Head of Systemic Risk Group, Citibank
Photo of Andrew Lo

Andrew Lo

Director, MIT Laboratory for Financial Engineering
Photo of Asu Ozdaglar

Asu Ozdaglar

Director, LIDS, MIT
Photo of Daron Acemoglu

Daron Acemoglu

Elizabeth and James Killian Professor, Economics, MIT

DARRYLL HENDRICKS

Chief Operating Officer, UBS Investment Bank
Photo of Devavrat Shah

Devavrat Shah

Professor, EECS, MIT

JAKE XIA

Chief Risk Officer, Harvard Management Company

James Darby Nielson

Managing Director of Research, Fidelity

John Guttag

Professor, MIT
Photo of Mark Flood

Mark Flood

Research Principal, Department of the Treasury, Office of Financial Research (OFR)
Photo of Munther Dahleh

Munther Dahleh

Director, IDSS, MIT

PAUL WILLEN

Senior Economist and Policy Advisor, Federal Reserve Bank of Boston

Paul Wojcik

Chief Risk Officer, T. Rowe Price

Peter Ferns

Technology Fellow and Senior Engineer, Goldman Sachs Enterprise Platforms Group

RONALD KAHN

Global Head of Scientific Equity Research, BlackRock
photo of Sandy Pentland

Sandy Pentland

Director, MIT Connection Science and Human Dynamics labs

SIDDHARTHA DALAL

Chief Data Scientist and Sr. VP of Advanced Research and Technology, AIG

Silvio Micali

Ford Professor of Engineering, MIT

Simon Johnson

Ronald A. Kurtz Professor of Entrepreneurship, MIT Sloan School of Management
Photo of Stephen Malinak

Stephen Malinak

Global Head of Content Analytics, Financial & Risk, Thomson Reuters

This workshop will focus on opportunities for big data and predictive analytics in finance and economics, including new approaches to modeling, measuring, and understanding risk.

Technological advancements have transformed financial markets into increasingly complex and dynamic systems. Organizations including banks, asset management firms, information technology firms, and regulatory agencies are all: increasingly interconnected; managing transactions at sub-millisecond time scales; and generating, storing, and managing masses of data. This creates challenges and opportunities when it comes to understanding and managing risk in financial systems — for example: How can we leverage big data to develop better analytical methods for predicting bubbles and collapses in financial systems? Can we use data to map and model interconnections across financials systems toward better understanding systemic risk and how disruptions propagate? What are the opportunities for creating new financial forecasting tools and risk metrics? This workshop will bring together stakeholders from across industry, government and academia to discuss these opportunities and priorities for research and innovation in this area.

This workshop will explore:

  • priorities for financial firms and regulators in assessing and managing risk;
  • opportunities for big data and machine learning in better understanding behavior and modeling the interconnections and complex dynamics in financial systems;
  • using statistical models and algorithms to improve decision-making and design policies;
  • novel applications for emerging technologies, including blockchain and encryption techniques;
  • applying new techniques to enable data sharing while managing privacy;
  • opportunities to collaborate on developing resources, open source tools and platforms, and new predictive analytic metrics that enable a deeper understanding of risk and new approaches to monitoring risk and improving stability in financial markets.

Invited participants will bring perspectives from across disciplines including finance, economics, statistics and data science, and social sciences. We will use this workshop to explore innovate ideas at the intersection of these fields and generate an output document that summarizes challenges and priorities for research.

Founded July 2015, the mission of MIT’s new Institute for Data, Systems, and Society (IDSS) is to advance education and research in state-of-the-art, analytical methods in information and decision systems; statistics and data science; and the social sciences, and to apply these methods to address complex challenges in areas such as finance, energy, urbanization, and health.

Sponsors

We are grateful to our sponsors for their generous support.

For more information about MIT IDSS and sponsorship, please contact: Elizabeth Bruce, Executive Director, MIT Institute for Data, Systems, and Society.