Data Analytic Methods for Institutional Discrimination Detection in Finance Applications

dc.contributor.author	Wilson, Carlton
dc.date.accessioned	2022-01-19T23:58:41Z
dc.date.available	2022-01-19T23:58:41Z
dc.date.issued	2021-04-30
dc.identifier.uri	http://hdl.handle.net/1920/12221
dc.description.abstract	Banks are slowly recovering from the COVID 19 pandemic. This is the effect of The American Rescue Plan of 2021. As a result, consumers are spending and putting money back into the economy. As we begin to see light at the end of the tunnel, this is good news for the banking industry and especially beneficial to the working-class people.Based on our research, we've discovered that a there are unorthodox approaches and factors that can contribute to help mapping and identifying the risk when calculating the credit score.	en_US
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	machine learning	en_US
dc.subject	finance	en_US
dc.title	Data Analytic Methods for Institutional Discrimination Detection in Finance Applications	en_US
dc.type	Working Paper	en_US