SMECreditPortal: A data driven financial risk profiling portal for SMEs for financial inclusion
| dc.contributor.author | Namatovu, Damalie | |
| dc.contributor.author | Tibaingana, Phoebe | |
| dc.contributor.author | Angella, Bernadette | |
| dc.contributor.author | Acom, Caroline | |
| dc.date.accessioned | 2019-11-15T10:44:48Z | |
| dc.date.available | 2019-11-15T10:44:48Z | |
| dc.date.issued | 2019-05 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/7191 | |
| dc.description | A project report submitted to the School of Computing and Informatics Technology for the study leading to a project in partial fulfillment of the requirements for the award of the Degree of Bachelor of Science in Software Engineering of Makerere University. | en_US |
| dc.description.abstract | This report describes the steps that were undertaken to develop the SMECreditPortal, a Credit Risk Analysis System to be used by financial institutions in determining the credit worthiness of SMEs requesting for loans. SMECreditPortal assesses the credit worthiness of SMEs based on risk factors identified in their business practices. These risk factors include the credit history of the client SME, turnover, sales inventory, and number of employees, among others. This is done with the help of machine learning. The credit score determines the likelihood of the SMEs repaying the loan. A high credit score means the SME is more likely to be able to repay the loan and the reverse is true. | en_US |
| dc.language.iso | en | en_US |
| dc.title | SMECreditPortal: A data driven financial risk profiling portal for SMEs for financial inclusion | en_US |
| dc.type | Thesis | en_US |