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Credit acceptance process strategy case studies - the power of Credit Scoring

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  • Karol Przanowski

Abstract

The paper is aware of the importance of certain figures that are essential to an understanding of Credit Scoring models in credit acceptance process optimization, namely if the power of discrimination measured by Gini value is increased by 5% then the profit of the process can be increased monthly by about 1 500 kPLN (300 kGBP, 500 kUSD, 350 kEUR). Simple business models of credit loans are also presented: acquisition - installment loan (low price) and cross-sell - cash loans (high price). Scoring models are used to optimize process, to become profitable. Various acceptance strategies with different cutoffs are presented, some are profitable and some are not. Moreover, in a time of prosperity some are preferable whilst the inverse is true during a period of high risk or crisis. To optimize the process four models are employed: three risk models, to predict the probability of default and one typical propensity model to predict the probability of response. It is a simple but very important example of the Customer Lifetime Value (CLTV or CLV) model business, where risk and response models are working together to become a profitable process.

Suggested Citation

  • Karol Przanowski, 2014. "Credit acceptance process strategy case studies - the power of Credit Scoring," Papers 1403.6531, arXiv.org.
    • Handle: RePEc:arx:papers:1403.6531
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    File URL: http://arxiv.org/pdf/1403.6531
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    References listed on IDEAS

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    1. Steven Finlay, 2010. "Credit Scoring, Response Modelling and Insurance Rating," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-29898-9, December.
    2. G Verstraeten & D Van den Poel, 2005. "The impact of sample bias on consumer credit scoring performance and profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 981-992, August.
    3. repec:rze:efinan:v:9:y:2012:i:1:p:44-59 is not listed on IDEAS
    4. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
    5. Banasik, John & Crook, Jonathan, 2007. "Reject inference, augmentation, and sample selection," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1582-1594, December.
    6. J Banasik & J Crook, 2005. "Credit scoring, augmentation and lean models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1072-1081, September.
    7. Karol Przanowski, 2013. "Banking Retail Consumer Finance Data Generator – Credit Scoring Data Repository," "e-Finanse", University of Information Technology and Management, Institute of Financial Research and Analysis, vol. 9(1), pages 44-59, May.
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