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

• Authors: M. Karwański , U. Grzybowska
• 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

89.65.Gh

Discipline

• 89.65.Gh: Economics; econophysics, financial markets, business and management(for economic issues regarding production and use of renewable energy, see 88.05.Lg)

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2016
• Volume: 129
• Issue: 5
• Pages: 945-949

Dates

published

2016-05

Contributors

author

M. Karwański

• Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Science, Nowoursynowska 159, PL-02-776 Warsaw, Poland

author

U. Grzybowska

• Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Science, Nowoursynowska 159, PL-02-776 Warsaw, Poland

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)

Identifiers

DOI

10.12693/APhysPolA.129.945