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Mitigating Default Risk in the Consumer Credit Market

Halén Dahlström, Jacob LU (2016) NEKN01 20161
Department of Economics
Abstract
This paper aims to evaluate recent policy updates in a credit scoring model and determine if the new model is efficient, as well as further investigate other potential risk factors. In order to evaluate the policy changes, the proprietary dataset is first categorized and estimated by a logistical regression model and secondly the dataset is transformed according to new policies and then simulated in a second regression. The choice of variables is further tested to ensure robust result of the identified risk factors and best fitting of the model. The discoveries points towards efficient implemented policy changes to the scoring model, and the identification of other potential risk factors which leads to a set of managerial suggestions.
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author
Halén Dahlström, Jacob LU
supervisor
organization
course
NEKN01 20161
year
type
H1 - Master's Degree (One Year)
subject
keywords
Credit scoring models, Probability of default, Logistical regression, Maximum likelihood, Information value
language
English
id
8891123
date added to LUP
2016-09-09 14:05:43
date last changed
2016-09-09 14:05:43
@misc{8891123,
  abstract     = {This paper aims to evaluate recent policy updates in a credit scoring model and determine if the new model is efficient, as well as further investigate other potential risk factors. In order to evaluate the policy changes, the proprietary dataset is first categorized and estimated by a logistical regression model and secondly the dataset is transformed according to new policies and then simulated in a second regression. The choice of variables is further tested to ensure robust result of the identified risk factors and best fitting of the model. The discoveries points towards efficient implemented policy changes to the scoring model, and the identification of other potential risk factors which leads to a set of managerial suggestions.},
  author       = {Halén Dahlström, Jacob},
  keyword      = {Credit scoring models,Probability of default,Logistical regression,Maximum likelihood,Information value},
  language     = {eng},
  note         = {Student Paper},
  title        = {Mitigating Default Risk in the Consumer Credit Market},
  year         = {2016},
}