Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 7 of 7 for “"loan default prediction"”.
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Leveraging Machine Learning and Causal Inference for Loan Default Prediction
… combined with causal inference to predict loan defaults in peer-to-peer lending. Accurately forecasting loan defaults is crucial for mitigating financial risk and optimizing lending strategies. This analysis is based on multiple datasets of loan applications spanning over a decade, …
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An exploration of alternative features in micro-finance loan default prediction models
… can be used to improve the performance of loan default prediction models. The improvement gained by using alternative features is measured by comparing loan default prediction models trained using only traditional credit scoring data to models developed using a combination of traditional …
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Identification of relevant predictors of loan default using the Elastic Net model
The timely prediction of loan default plays an important role in lending decisions and monitoring loans. However, there has been little development of models for the selection of relevant variables for the prediction of loan default. This study identifies financial and economic indicators for the …
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Prediction of going-concern status: a probit model for the auditors
… No. 34 are inadequate and existing going-concern prediction models are flawed. In view of this, the objective of the dissertation is to construct a going-concern prediction model (hereafter called the Koh model) that is based upon improved statistical techniques and methodology. A sample of 165 …
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An AI-driven loan brokerage platform: integrating socio-economic factors, consumer financial behaviour, and multi-criteria decision analysis for responsible lending.
The loan settlement process is a critical yet complex aspect of financial intermediation, involving multiple stages of decision-making that impact both lenders and borrowers. Traditional approaches to loan assessment rely heavily on manual review or simplified credit scoring mechanisms that …