Business Analytics Using Random Forest Trees for Credit Risk Prediction: A Comparison Study

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Provided by: Science & Engineering Research Support soCiety (SERSC)
Topic: Enterprise Software
Format: PDF
In the era of stringent and dynamic business environment, it is crucial for organizations to foresee their clients' delinquency behavior. Such environment and behavior create unreliable base for strategic planning and risk management. Business analytics combines the business expertise and computer intelligence to assist the decision makers by predicting an individual's credit status. This empirical research aims to evaluate the performance of different machine Learning algorithms for credit risk prediction with more focus on random forest trees. Several experiments inspired by observation and literature illustrate the potentials of computer-based model in classifying a number of bank history records.
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