A Loan default prediction and loan amount recommendation tool for SACCOs in Nairobi: a case of Okoa Management SACCO

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dc.contributor.authorMwalozi, P. M.
dc.date.accessioned2023-10-06T07:18:49Z
dc.date.available2023-10-06T07:18:49Z
dc.date.issued2023
dc.descriptionFull- text thesis
dc.description.abstractSACCOs loan delinquency is a severe danger to the organization's capacity to continue availing loans to loan applicants and to grow. SACCOs are unable to collect what they have lent out to loan beneficiaries as the default rate rises gradually. This research project aimed at using the analysis of the different factors that determine loan defaults in microfinance institu-tions, microlending institutions and SACCOs in Kenya with a focus on Okoa Management Ltd. and how the same factors can be used to predict the likelihood of a loan borrower to default in the repayment process by applying machine learning algorithms. Credit risk assessment pre-cision is important to the functioning of lending institutions. Traditional and most existing credit score models are developed and designed using demographic characteristics, historical payment data, credit bureau data and application data, with most of them not suitable for de-veloping countries such as Kenya which consider the employment type (casual, temporary, contractual or permanent) and the fact that we can lend up to 3 times as much as the borrower’s savings. With these factors being constantly changing and dynamic, credit risk models based on machine learning algorithms provide a higher level of accuracy in predicting default as they can be continuously trained with new data sets should the variables that are used change. Risk management has been an increasing issue for credit lending institutions as the need to deter-mine the likelihood of defaulting by borrowers is becoming more evident. By using machine learning, we can be able to reduce the uncertainty that comes with borrowing and even go further to recommending lower amounts for borrowers who we predict are likely to default in the repayment of the loan amount they have in mind. The research focused on three main al-gorithms: logistic regression, decision trees and tensor flow on the prediction. The algorithm that provided the best accuracy was the decision tree. The results of the research showed that people with little or no collateral (home-ownership/car ownership) were more likely to default and that there was a low correlation between months since last delinquent and the loan predic-tion default likelihood status. Keywords: Loan default prediction, machine learning, credit lending
dc.identifier.citationMwalozi, P. M. (2023). A Loan default prediction and loan amount recommendation tool for SACCOs in Nairobi: A case of Okoa Management SACCO [Strathmore University]. http://hdl.handle.net/11071/13528
dc.identifier.urihttp://hdl.handle.net/11071/13528
dc.language.isoen
dc.publisherStrathmore University
dc.titleA Loan default prediction and loan amount recommendation tool for SACCOs in Nairobi: a case of Okoa Management SACCO
dc.typeThesis
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