PL EN


Preferences help
enabled [disable] Abstract
Number of results
2016 | 129 | 5 | 945-949
Article title

Propensity Score Matching and Its Application to Risk Drivers Detection in Financial Setting

Content
Title variants
Languages of publication
EN
Abstracts
EN
In credit risk scoring models are used as a tool to evaluate the level of risk associated with applicants or customers. The aim of these models is not only to estimate the probability that the client will not be able to fulfill his financial commitments but also to identify and estimate the risk drivers i.e., client attributes that are responsible for risk occurrence. Unfortunately, scoring models are built based on historic data stored by bank over the clients. Selection of clients is not random. This leads to systematic errors. Therefore one seeks methods that allow for a model correction that enables application of statistical inference. Quasi-experimental designs are practical solutions to this dilemma. One of such methods is propensity score matching. Propensity score matching allows also for detecting risk drivers that are independent of borrowers attributes, e.g., triggered by various bank strategies. The aim of our research is to apply propensity score matching methodology to identify these risk drivers in credit risk that could not be detected e.g., by regression models.
Keywords
EN
Year
Volume
129
Issue
5
Pages
945-949
Physical description
Dates
published
2016-05
References
  • [1] L. Ding, R. Quercia, J. Ratcliffe, J. Real Estate Res. 33, 245 (2011)
  • [2] P.R.Rosenbaum, D.B. Rubin, Biometrika 70, 41 (1983)
  • [3] P.R. Rosenbaum, D.B. Rubin, J. Am. Stat. Assoc. 79, 516 (1983)
  • [4] A. Bryson, R. Dorsett, S. Purdon, The use of propensity score matching in the evaluation of active labour market policies, 2002 http://dwp.gov.uk/asd/asd5/WP4
  • [5] R. Trzciński, The Use of Propensity Score Matching Technique in Evaluation Research, Polska Agencja Rozwoju Przedsiębiorczości, Warszawa 2009 (in Polish)
  • [6] M. Li, Org. Res. Meth. 39, 1 (2012), doi: 10.1177/1094428112447816
  • [7] A. Saunders, S. Steffen, Rev. Fin. Stud. 24, 4091 (2011), doi: 10.1093/rfs/hhr083
  • [8] C. Heinrich, A. Maffioll, G. Vazquez, A Primer for Applying Propensity Score Matching Impact-Evaluation Guidelines, Technical Notes No. IDB-TN-161 2010
  • [9] D. Larose, Data Mining Methods and Models, Wiley, New Jersey 2006, doi: 10.1002/0471756482
  • [10] T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning. Data Mining, Inference and Prediction, 2nd ed., Springer, New York 2009, doi: 10.1007/978-0-387-21606-5
  • [11] J. Luellen, W. Shadish, M. Clark, Evaluat. Rev. 29, 530 (2005)
Document Type
Publication order reference
YADDA identifier
bwmeta1.element.bwnjournal-article-appv129n510kz
Identifiers
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.