Concerns about spreading the virus have shifted many transactions to digital formats – either contactless payments or online pre-orders. This article explores the financial inclusion effects of a cash-less economy.
Nancy Scola, Politico
The IRS has had trouble getting stimulus money to people quickly because of reliance on a network of tax preparation intermediaries to distribute funds. This article explores whether this infrastructure disproportionately affected low- and moderate-income people who were eligible for relief.
Paul Kiel, Justin Elliot and Will Young, Pro Publica
This article charts how FinTech companies are deploying new technology pilot programs to aid in COVID recovery. The article references several companies deploying big data analysis capabilities and notes uses of distributed ledgers for settling trades.
Peter Feltman, Roll Call
This IMF blog post from the IMF considers one pathway for protecting the global banking system amid the pandemic. Pointing to the experience of the 2008 crisis and recovery, the authors recommend requiring transparent loss reporting, relying on capital and liquidity buffers to support continued lending, and encouraging loan modification.
Tobias Adrian and Aditya Narain, IMF Blog
This article addresses risks related to the use of artificial intelligence and machine learning models that were trained on and use data that overweight benign credit conditions.
Jacob Kosoff, American Banker
This brief from the World Economic Forum evaluates how the use of personal data – and business models for monetizing that data – might evolve as firms respond to the pandemic. For example, machine learning algorithms previously deployed to analyze consumer tastes could be deployed to track viral detection.
Murat Sönmez
This report analyzes mortgage default using information taken from the JPMorgan Chase Institute housing finance research to evaluate the relationship between liquidity, equity, income level, and payment burden and default. Across all four groups, the report finds that liquidity may be more predictive for determining the likelihood of mortgage default particularly among borrowers with little post-closing liquidity and little liquidity but high equity. Overall, the report determines that alternative underwriting standards incorporating a minimum amount of post-closing liquidity may be a more effective way to prevent mortgage default compared to using DTI thresholds at origination.
Diana Farrell, Kanav Bhagat, and Chen Zhao
This paper provides a nontechnical overview of common machine learning algorithms used in underwriting credit. It provides a review – including strengths and weakness – of common machine learning techniques in credit underwriting, including tree-based models, support vector machines, and neural networks. The paper also considers the financial inclusion implications of machine learning, nontraditional data, and fintech.
Majid Bazarbash, International Monetary Fund (IMF)
This paper provides an overview of explainable machine learning as well as definitions of explainable artificial intelligence, examples of its usage, and details for responsible and human-centered use.
Patrick Hall, H20.AI
This white paper outlines some of the most important considerations for managing risk in machine learning models to create more accurate and compliant algorithms. Key recommendations include focusing on the quality of input data as well as implementing techniques to reduce and expose bias.
Andrew Burt, Immuta & Future of Privacy Forum
