When the Lender Extends a Helping Hand: Native CDFI Client Counseling and Loan Performance in Indian Country
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DOI: 10.1007/s41996-023-00119-x
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References listed on IDEAS
- Stefania Albanesi & Domonkos F. Vamossy, 2019.
"Predicting Consumer Default: A Deep Learning Approach,"
Papers
1908.11498, arXiv.org, revised Oct 2019.
- Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.
- Albanesi, Stefania & Vamossy, Domonkos, 2019. "Predicting Consumer Default: A Deep Learning Approach," CEPR Discussion Papers 13914, C.E.P.R. Discussion Papers.
- Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," NBER Working Papers 26165, National Bureau of Economic Research, Inc.
- Butaru, Florentin & Chen, Qingqing & Clark, Brian & Das, Sanmay & Lo, Andrew W. & Siddique, Akhtar, 2016.
"Risk and risk management in the credit card industry,"
Journal of Banking & Finance, Elsevier, vol. 72(C), pages 218-239.
- Florentin Butaru & QingQing Chen & Brian Clark & Sanmay Das & Andrew W. Lo & Akhtar Siddique, 2015. "Risk and Risk Management in the Credit Card Industry," NBER Working Papers 21305, National Bureau of Economic Research, Inc.
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More about this item
Keywords
G21; G53; G11; J15; O16; P43;All these keywords.
JEL classification:
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- G53 - Financial Economics - - Household Finance - - - Financial Literacy
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
- O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
- P43 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Finance; Public Finance
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