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“The use of Machine Learning (ML) models is gaining traction in finance due to their better predictive capacity compared to traditional statistical techniques…One of the use cases with greater potential is its application to credit underwriting and scoring, since by having better predictive capacity, ML models allow better estimates of the probability of default and therefore could result in more accurate credit scores. But this improvement in predictive performance does not come without risk.”
“Artificial intelligence and machine learning analyses are driving critical decisions impacting our lives and the economic structure of our society. These complex analytical techniques—powered by sophisticated math, computational power, and often vast amounts of data—are deployed in a variety of critical applications, from making healthcare decisions to evaluating job applications to informing parole and probation decisions to determining eligibility and pricing for insurance and other financial services.”
FinRegLab attended ICML’s Responsible Decision Making in Dynamic Environments workshop and presented our work on a subsection of our white paper. The workshop poster and presentation focused on how model diagnostic tools affect lenders’ ability to manage fairness concerns related to identifying less discriminatory alternative models used to extend credit.
“The report “examines the central role of finance in the economic recovery from COVID-19. Based on an in-depth look at the consequences of the crisis most likely to affect low- and middle-income economies, it advocates a set of policies and measures to mitigate the interconnected economic risks stemming from the pandemic—risks that may become more acute as stimulus measures are withdrawn at both the domestic and global levels.”
FinRegLab, in partnership with the U.S. Department of Commerce, the National Institute of Standards and Technology (NIST), and the Stanford Institute for Human-Centered Artificial Intelligence (HAI), are hosting a symposium, bringing together leaders from government, industry, civil society, and academia to explore potential opportunities and challenges posed by artificial intelligence and machine learning deployment across different economic sectors, with a particular focus on financial services and healthcare.
“Prospective borrowers with less wealth and little credit history are now deemed riskier by the automated underwriting systems that dominate mortgage lending these days. As a result, they tend to be denied more often or given higher interest rates … despite the fact that they might well be capable of responsibly making mortgage payments.”