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Advancing financial inclusion: a credit risk model for customers with adverse credit history
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This thesis addresses the challenge of effectively assessing credit risk for customers with
unfavorable financial histories. The goal is to develop a model that can segment these
customers based on various factors such as payment history, income, debt-to-income ratio,
and behavioral patterns, in order to determine the likelihood of defaulting on new credit card
payments. Machine learning algorithms, including decision trees, random forests, and neural
networks, are employed to construct the model.
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Credit risk assessment Financial inclusion Fintech Client segmentation Machine learning Financial markets