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.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. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12221 | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.subject | Machine learning | |
| dc.subject | Finance | |
| dc.title | Data Analytic Methods for Institutional Discrimination Detection in Finance Applications | |
| dc.type | Working Paper |