Statistical Challenges in Retail Credit Analysis

  • David J. HandEmail author


The retail credit domain is characterised by data sets which are large in terms of number of cases, number of variables, and acquisition rate. Furthermore, the area presents many novel statistical and mathematical challenges, requiring the development of new methods. This paper outlines some of the areas in which the Consumer Credit Research Group has contributed to the industry over recent years, including developing new measures of loan application scorecard performance, tools for detecting fraudulent credit card transactions, and methods for tackling selection bias in fraud and other areas.


Random Forest Credit Card Gini Coefficient Score Distribution Kolmogorov Smirnov 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Acknowledgement of sponsors

Many bodies have sponsored the work of the Consumer Credit Research Group, including the EPSRC, ESRC, GMAC, HBOS, British Credit Trust, Capital One, Fair Isaac, Goldman Sachs, Barclaycard, Littlewoods, Barclays Direct Loan Division, Abbey National, Institute of Actuaries, Link Financial, Shell, and others. We are most grateful to all of them, for their vision and encouragement during our research.


  1. 1.
    Hand, D.J.: Measuring classifier performance: a coherent alternative to the area under the ROC curve. Mach. Learn. 77, 103–123 (2009)CrossRefGoogle Scholar
  2. 2.
    Hand, D.J., Adams, N.M.: Selection bias in credit scorecard evaluation. J. Oper. Res. Soc. 65, 408–415 (2014)CrossRefGoogle Scholar
  3. 3.
    Hand, D.J., Anagnostopoulos, C.: When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance? Pattern Recognit. Lett. 34, 492–495 (2013)CrossRefGoogle Scholar
  4. 4.
    Hand, D.J., Crowder, M.J.: Overcoming selectivity bias in evaluating new fraud detection systems for revolving credit operations. Int. J. Forecast. 28, 216–223 (2012)CrossRefGoogle Scholar
  5. 5.
    Krzanowski, W.J., Hand, D.J.: Testing the difference between two Kolmogorov-Smirnov values in the context of receiver operating characteristic curves. J. Appl. Stat. 38, 437–450 (2011)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of MathematicsImperial College, South Kensington CampusLondonUK

Personalised recommendations