Credit Modeling
39 items from mourarthur/awesome-credit-modeling ★176
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Availability of Credit to Small Businesses federalreserve.gov
Section 2227 of the Economic Growth and Regulatory Paperwork Reduction Act of 1996 requires that, every five years, the Board of Governors of the Federal Reserve System submit a report to the Congress detailing the extent of small business lending by all creditors. The most…
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Machine learning predictivity applied to consumer creditworthiness fbj.springeropen.com
Analyzes the adequacy of borrower’s classification models using a Brazilian bank’s loan database, exploring machine learning techniques, and comparing their predictive accuracy with a benchmark based on a Logistic Regression model. Comparisons are based on usual classification…
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Reject inference in application scorecards: evidence from France econpapers.repec.org
Good introduction and discussion on the topic.
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Financial credit risk assessment: a recent review dl.acm.org
Summarizes the traditional statistical models and state-of-the-art intelligent methods for financial distress forecasting, with emphasis on the most recent achievements.
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Fintech and big tech credit: a new database bis.org
This Working Paper by the Bank of International Settlements, while not as focused on credit risk, maps the conditions for and niches occupied by alternative credit, be it provided by fintechs or big tech companies.
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Machine learning explainability in finance: an application to default risk analysis bankofengland.co.uk
This Staff Working Paper from the Bank of England proposes a framework for addressing the ‘black box’ problem present in some Machine Learning (ML) applications.
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Presents the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. These alternative data sources have shown themselves to be immensely powerful in predicting borrower behavior in segments…
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Modeling Institutional Credit Risk with Financial News arxiv.org
Current work in downgrade risk modeling depends on multiple variations of quantitative measures provided by third-party rating agencies and risk management consultancy companies. There has been a wide push into using alternative sources of data, such as financial news, earnings…
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Consumer credit-risk models via machine-learning algorithms alo.mit.edu
The authors apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. They are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults.
