Dig Deep into Financial Supply Chains

Women Leading Research: Louiqa Raschid

SMITH BRAIN TRUST – To better understand the subprime mortgage debacle’s role in the financial crisis of 2008, one has to dissect the underlying financial supply chain. In the context of a subprime crisis, this involves unraveling the actions taken by financial entities in creating and selling  U.S. residential mortgage backed securities (resMBS).  Such a study is described in a working paper, “Probabilistic Community Models to Understand Financial Supply Chains,” co-authored by information systems professor Louiqa Raschid at the University of Maryland’s Robert H. Smith School of Business.

The financial institutions and their roles in resMBS contracts are deeply embedded in the text of prospectuses filed with the Securities Exchange Commission. Such filings typically “are hundreds of pages of semi-structured text and pose a challenge to create datasets for analytics,” Raschid says.

Countering such a void, she and her team collaborated with researchers at IBM to create “a novel and complex financial dataset" based on about 5,000 resMSB prospectuses filed between 2001 and 2008. The team built machine learning models from the extracted data that led them to explore the impact of “upstream toxic financial communities” on the complex, resMBS products downstream.

"The work may prove to be groundbreaking, in showing the synergistic potential of utilizing financial big data and computational methods to understand complex financial ecosystems,” write Raschid and her co-authors, Margrét Bjarnadóttir, Smith School assistant professor of management science and statistics; Zheng Xu, UMD computer science PhD candidate; and Joe Langsam, senior lecturer at the MIT Sloan School of Management and policy fellow for the Smith School’s Center for Financial Policy.

Raschid is also organizing and directing an ongoing Financial Entity Identification and Information Integration Challenge in collaboration with NIST “to provide interesting datasets to researchers at the intersection of finance and big data.”

Louiqa Raschid is a professor of information systems for the University of Maryland’s Robert H. Smith School of Business and holds appointments with UMD’s Institute for Advanced Computing Studies (UMIACS) and the Department of Computer Science. 

Research interests: Data management and data science, with a strong link to diverse applications, including the life sciences and health sciences, humanitarian disaster relief applications, human behavior modeling within social streams, and the modeling and management of financial ecosystems.

Selected accomplishments: About 150 papers in leading journals/conferences and covering databases, scientific computing, Web data management, bioinformatics and artificial intelligence; research awards including more than 25 grants from the National Science Foundation and DARPA; founding director of the Sahana FOSS project for disaster information management; evangelist for financial data science; Fellow of the ACM.

About this series: The Smith School faculty is celebrating Women’s History Month 2018 in partnership with ADVANCE, an initiative to transform the University of Maryland by investing in a culture of inclusive excellence. Daily faculty spotlights support activities from the school’s Office of Diversity Initiatives, starting with the seventh annual Women Leading Women forum on March 1, 2018.

Other fearless ideas from:  Rajshree Agarwal  |  Ritu Agarwal  |  T. Leigh Anenson  |  Kathryn M. Bartol  |  Christine Beckman  |  Margrét Bjarnadóttir  |  M. Cecilia Bustamante  |  Jessica M. Clark  |  Rellie Derfler-Rozin  |  Waverly Ding  |  Wedad J. Elmaghraby  |  Rosellina Ferraro  |  Rebecca Hann  | Amna Kirmani  |  Hanna Lee  |  Hui Liao  |  Jennifer Carson Marr  |  Wendy W. Moe  |  Courtney Paulson  |  Louiqa Raschid  |  Rebecca Ratner  |  Rachelle Sampson  |  Debra L. Shapiro  |  M. Susan Taylor  |  Niratcha (Grace) Tungtisanont  |  Vijaya Venkataramani  |  Janet Wagner  |  Yajin Wang  | Liu Yang  |  Jie Zhang  |  Lingling Zhang