The OFR works with public and private organizations to achieve its goals in monitoring, researching, and analyzing financial stability. Research partnerships and catalyzed research programs are an effective way to develop high-impact research and methods for analyzing emerging and existing risks. These efforts allow the OFR to leverage its resources to consider a wider range of issues than might otherwise be possible.
Views and opinions expressed are those of the authors and do not represent official positions or policy of the Department of the Treasury (Treasury) or the OFR. The written publications (including but not limited to research papers, briefs, articles and blogs) highlighted on this page are not authored by the OFR and are owned by a third party. The OFR does not guarantee that statements or data contained in the written publications are accurate and is not responsible for updating these products.
National Science Foundation grant to the National Bureau of Economic Research
OFR has partnered with the National Science Foundation (NSF) to fund grant awards in support of financial stability related research activities. NSF used funds provided by OFR to award a grant to the National Bureau of Economic Research (NBER), a nonprofit organization committed to disseminating unbiased economic research among public policymakers, business professionals, and the academic community. By partnering with NSF to fund a grant award to NBER, the OFR is seeking insight from a research community that is actively involved in cutting-edge research into major economic issues, including financial stability.
NBER Catalyzed Research Projects & Conferences
Financial Frictions and Systemic Risk Project
This project supports research on the interplay between financial market institutions, financial frictions, and systemic financial risk. Particular issues of interest include funding structures and capital market frictions; operational and financial linkages across markets; financial stability; and the determinants, detection, and remediation of systemic risk. The initiative encourages interaction among researchers, policymakers, and financial market practitioners, with the goal of identifying and addressing research questions that are particularly important for public policy.
Financial Market Frictions and Systemic Risks Conference (March 8, 2024)
This conference brought together researchers in various subfields of economics and financial economics to study funding structures, capital market frictions, operational and financial linkages across markets, financial stability, and systemic risk. The conference promoted knowledge about potential sources of data that might be used to address these topics and to advance interactions between researchers, policy-makers, practitioners, and regulators, with the goal of identifying and addressing research questions that bear on public policy.
Defense Advanced Research Projects Agency
Cybersecurity vulnerabilities in the financial system continue to be a serious and evolving threat to financial stability. To increase visibility in this area, the OFR partnered with the Defense Advanced Research Projects Agency (DARPA)—a research and development agency of the United States Department of Defense responsible for the development of emerging technologies for use by the U.S. and its allies—to develop research on risks to the U.S. financial system from a cyberattack.
DARPA’s Ensuring Consistency of Systemic Information (ECoSystemic) program aims to develop innovative techniques for the robust recovery of financial information systems. Federated information systems are large, complex, and distributed computer and data systems, exactly the systems on which today’s financial system relies. With improved backups and processes for recovering data in the event of disruption or corruption, information systems can promptly return to a functional and mutually consistent restored state. ECoSystemic has engaged teams, each exploring a different facet of the financial system, and developing distinct analytical approaches and tools. Each team produces an executive summary of its work and results. The techniques developed for maintaining resilient datasets have applications in the military and commercial arenas as well.
DARPA Partnership Products
OFR did not fund these DARPA-authored products, but OFR provided advice and feedback on their technical approaches.
Resilient Knowledge Graph Representations for Federated Financial Data
This paper presents a new data paradigm that can facilitate analyses of critical financial issues. Specifically, the paper examines how the widespread use of resilient data structures could enhance the efficiency and stability of financial markets by allowing regulators and market participants to understand and better identify systemic risks. The ability to obtain value from data depends on how easily the data can be accessed for their intended use. A knowledge graph organizes federated data that lends itself to understanding relationships among entities such as market participants, exchanges, or instruments. Compared to other data structures, such as flat files or relational databases, knowledge graphs are more extensible, have lower barriers to access, and are uniquely suited to identifying relations within networks for visualization and analysis. The research shows that knowledge graphs also can be made resilient to attacks by malicious actors and physical failures. The paper demonstrates through examples how knowledge graphs can be leveraged to derive resilient meta statistics that financial regulators can use to identify abnormal behaviors and unusual variations in financial database characteristics over time.
Improving the resilience of machine learning in financial systems through synthetic data
The stability of a financial system requires the ability to recognize and recover from catastrophic events quickly. It requires that data backups and their connected systems are consistent. This inference problem requires a model of why acceptable differences exist to detect when inconsistencies arise. Synthetic data that systematically generate acceptable and unacceptable inconsistencies can significantly improve the financial system’s resilience. This paper outlines an interpretable procedure using Bayesian probabilistic models to create synthetic data. The approach allows the development of machine learning tools to detect inconsistencies in federated backups. The paper shows how this synthetic-data approach can reveal the conditions under which a machine-learning tool may fail and how that information can be used to build a more robust tool for detecting potential operation outages or cybersecurity threats.
Protecting Distributed Financial Networks
Distributed financial networks are a feature of the international financial system of payments, but they are also increasingly vulnerable to disruption as new technologies create unexpected opportunities for surprises, threats, and shocks. These vulnerabilities arise due to current economic and technical trends, including the increasing velocity and digitalization of individual economic activity, as well as the growing interconnectedness of the global economy. This brief discusses these financial system challenges through the lens of a credit card payment system. It present a range of integrated tools and procedures tailored to meet the needs of the financial firm, network, and system as no single “silver bullet” solution exists. Instead, protecting networks requires multiple, integrated solutions that work together to reduce system fraud and errors.