id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_ogq5r3sr6vb2dkhfp4speay7dq	Maria Rocha Sousa	A new dynamic modeling framework for credit risk assessment	2016	11	.pdf	application/pdf	12713	1942	57	We propose a new dynamic modeling framework for credit risk assessment that extends the prevailing credit scoring models built upon historical data static settings. modeling consists of sequential learning from the new incoming data. Developing and implementing a credit scoring model can be time likely to default in a future fixed period, 1 year for PD models (Thomas, 2010; Thomas, Edelman, & Crook, 2002). sed in supervised learning, and specifically in credit score modeling. In this work we import some of the emerging techniques in conept drift adaptation into credit risk assessment models. nd test a new dynamic framework to model credit risk. This research presents a new modeling framework for credit risk A new dynamic credit scoring model A new dynamic modeling framework for credit risk assessment A new dynamic modeling framework for credit risk assessment A new dynamic modeling framework for credit risk assessment	./cache/work_ogq5r3sr6vb2dkhfp4speay7dq.pdf	./txt/work_ogq5r3sr6vb2dkhfp4speay7dq.txt