Bias prevention in Loan Applications by Using Machine Learning Models
| dc.contributor.author | Zaid, Altukhi | |
| dc.contributor.author | Kordu, Sushma Sree | |
| dc.contributor.author | Muraleedharakumar, Dipukumar | |
| dc.contributor.author | Wilson, Carlton | |
| dc.date.accessioned | 2022-01-19T12:52:16Z | |
| dc.date.available | 2022-01-19T12:52:16Z | |
| dc.date.issued | 2021-04-28 | |
| dc.description.abstract | Creditworthiness is based on how the borrower handled debt and credit. Creditworthiness is how a lender decides if the person or company who requests for money can repay the loan that will be borrowed. The first step to get a loan is to complete and fill an application.The main aim of this research is to use attributes such as loan type, credit history, credit amount, employment history status, education background, marital status, the duration of the loan, and the current status of checking or savings account etc. to come up with an alternate mechanism for determining creditworthiness. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12189 | |
| dc.language.iso | en_US | |
| 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 | Credit Score | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Finance | |
| dc.title | Bias prevention in Loan Applications by Using Machine Learning Models | |
| dc.type | Working Paper |