Support vector machines for classifying and describing credit applicants: detecting typical and critical regions

  title={Support vector machines for classifying and describing credit applicants: detecting typical and critical regions},
  author={Klaus B. Schebesch and Ralf Stecking},
Credit applicants are assigned to good or bad risk classes according to their record of defaulting. Each applicant is described by a high-dimensional input vector of situational characteristics and by an associated class label. A statistical model, which maps the inputs to the labels, can decide whether a new credit applicant should be accepted or rejected, by predicting the class label given the new inputs. Support vector machines (SVM) from statistical learning theory can build such models… CONTINUE READING
Highly Cited
This paper has 148 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 33 extracted citations

Recent developments in consumer credit risk assessment

European Journal of Operational Research • 2007
View 3 Excerpts
Highly Influenced

A Novel Greedy Randomized Dynamic Ensemble Selection Algorithm

Neural Processing Letters • 2017
View 1 Excerpt

148 Citations

Citations per Year
Semantic Scholar estimates that this publication has 148 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-7 of 7 references

Informative patterns for credit scoring using linear SVM

R Stecking, KB Schebesch
Weihs C and Gaul W (eds). Classification—The Ubiquitous Challenge • 2005

Support vector machines for credit scoring: extension to non standard cases

KB Schebesch, R Stecking

Readings in Credit Scoring: Recent Developments, Advances, and Aims

LC Thomas, DB Edelman, JN Crook

Credit Scoring and its Applications

LC Thomas, DB Edelman, JN Crook
SIAM Monographs on Mathematical Modeling and Computation • 2002

Support Vector Machines

N Cristianini, J Shawe-Taylor

Similar Papers

Loading similar papers…