A self tuning model for risk estimation

Authors:
Robert J. Elliott;Alexei Filinkov
Affiliations:
Haskayne School of Business, University of Calgary, Calgary, Alb., Canada T2N 1N4;DSTO Edinburgh, South Australia and School of Mathematical Sciences, University of Adelaide, SA 5005, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 3
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Abstract

Credit scoring models often use linear or logistic regression to investigate the relation between observed characteristics and credit ratings. The basic relation is, however, a form of Bayes' theorem. This paper proposes a model in which estimation techniques from hidden Markov models are adapted to evaluate the parameters of a risk profile. The risk being estimated might be financial, as in credit scoring, or alternatively whether an observed member of a population might represent some terrorist threat.