Predictive modeling in credit risk: a survival analysis case
Omoga, Allan Anyona
Predictive modeling in credit risk: a survival analysis case - Nairobi Strathmore University 2017 - viii,39p;
Six survival analysis techniques are accessed by applying the techniques to a dataset consisting of 33,238 active credit facilities from a financial institution operating in Kenya. Namely, the Accelerated Failure Time (AFT) Models, Cox proportional hazard (PH) Model and the Mixture Cure Model (MCM) are considered in the comparisons. Evaluation of the techniques is conducted from a Statistical approach evaluation using the Area under the Curve (AUC) and financial evaluation using the annuity theory. The Cox Proportional Hazard (PH) and the Mixture cure model performs significantly well.
URI
Credit risk modeling
HG3751.O46 2017
Predictive modeling in credit risk: a survival analysis case - Nairobi Strathmore University 2017 - viii,39p;
Six survival analysis techniques are accessed by applying the techniques to a dataset consisting of 33,238 active credit facilities from a financial institution operating in Kenya. Namely, the Accelerated Failure Time (AFT) Models, Cox proportional hazard (PH) Model and the Mixture Cure Model (MCM) are considered in the comparisons. Evaluation of the techniques is conducted from a Statistical approach evaluation using the Area under the Curve (AUC) and financial evaluation using the annuity theory. The Cox Proportional Hazard (PH) and the Mixture cure model performs significantly well.
URI
Credit risk modeling
HG3751.O46 2017