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Full-Text Articles in Law
Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander
Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander
School of Business: Faculty Publications and Other Works
Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, …
Towards A Data-Driven Financial System: The Impact Of Covid-19, Nydia Remolina
Towards A Data-Driven Financial System: The Impact Of Covid-19, Nydia Remolina
Centre for AI & Data Governance
The COVID-19 outbreak has a growing impact on the global economy and the financial sector, which plays a critical role in mitigating the unprecedented macroeconomic and financial shock caused by the pandemic. Given the unprecedented nature of the current crisis, financial regulators and supervisors, central banks, along with governments and legislatures face challenges to maintain financial stability, preserve the well-functioning core markets, and ensure the flow of credit to the real economy. Even though the COVID-19 has slowed down our daily lives and stopped the operation of many industries, it did not have the same effect in the data-driven finance …
Big Data In Finance: Highlights From The Big Data In Finance Conference Hosted At The University Of Michigan October 27-28, 2016, Michael S. Barr, Brian Koziara, Mark D. Flood, Alfred Hero, H. V. Jagadish
Big Data In Finance: Highlights From The Big Data In Finance Conference Hosted At The University Of Michigan October 27-28, 2016, Michael S. Barr, Brian Koziara, Mark D. Flood, Alfred Hero, H. V. Jagadish
Law & Economics Working Papers
How can financial data be made more accessible and more secure, as well as more useful to regulators, market participants, and the public? As new data sets are created, opportunities emerge. Vast quantities of financial data may help identify emerging risks, enable market participants and regulators to see and better understand financial networks and interconnections, enhance financial stability, bolster consumer protection, and increase access to the underserved. Data can also increase transparency in the financial system for market participants, regulators and the public. These data sets, however, can raise significant questions about security and privacy; ensuring data quality; protecting against …