A Centralized credit scoring prototype for micro lending institutions in Kenya

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dc.contributor.authorKaringithi, Law Maina
dc.date.accessioned2021-10-15T15:15:08Z
dc.date.available2021-10-15T15:15:08Z
dc.date.issued2020-06
dc.descriptionA Dissertation Submitted in partial fulfilment of the requirements for the award of Degree of Master of Science in Mobile Telecommunication and Innovationen_US
dc.description.abstractMicrolending involves giving small loans to people in need. Usually, these loans are issued to entrepreneurs or those who need extra cash to either expand their businesses or for personal use. Digital lending is becoming a leading source of credit especially to low-income citizens with minimal or no financial footprints in Kenya and other parts of the world. It has quickly become the default way for lenders to service loan requests from borrowers due to the convenience it brings about as well as the increased number of requests that can be processed compared to the traditional way that required quite an amount of paper work. As the number of lending companies grows, there is the need to standardize the credit scoring process and maintain an updated credit activity log for every user. This ensures that lenders are always aware of any other unsettled debts a borrower might have and provides them with the most recent information to assess the risk they face by lending to a borrower. The proposed solution which is a representational state transfer (REST) based web service allows lenders to submit details of loans they have approved and issued to a borrower. The information is used to generate and keep track of the user’s credit score and amount of risk lenders face should they consider to lend to the user. Agile development methodology was used to develop a robust credit scoring prototype and an Android mobile application. The final prototype was tested to ensure that the requirements were met and the functionality working as required. Results of the tests showed the system was able to generate user credit scores and differentiate between good and bad borrowers at an accuracy level of 92%. The average response time for querying a user’s credit score was slightly below a second and this allows lenders to get a response form the system almost instantly. During tests conducted, no system failure or downtime was experienced.en_US
dc.identifier.urihttp://hdl.handle.net/11071/12172
dc.language.isoen_USen_US
dc.publisherStrathmore Universityen_US
dc.subjectMicro-lendingen_US
dc.subjectCredit scoringen_US
dc.subjectAndroiden_US
dc.titleA Centralized credit scoring prototype for micro lending institutions in Kenyaen_US
dc.typeThesisen_US
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MMTI Theses and Dissertations (2020)