000			01228nam a22001937a 4500
001			311837
005			20260707210020.0
999			_c311837 _d283093
008			171206b xxu||||| |||| 00| 0 eng d
050			_aHG3751.O46 2017
100			_aOmoga, Allan Anyona _9405932
245			_aPredictive modeling in credit risk: a survival analysis case
260			_aNairobi _bStrathmore University _c2017
300			_aviii,39p;
520			_aSix 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
650			_aCredit risk modeling _9405933
700			_aSamuel Mwalili (Prof.) _9405934
856			_uhttps://su-plus.strathmore.edu.ezproxy.library.strathmore.edu/handle/11071/5622
942			_2lcc _cTH _uMWK _zMSc-SS