Skip to main content
Article thumbnail
Location of Repository

Consumer finance: challenges for operational research

By Lyn C. Thomas


Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

Year: 2010
OAI identifier:
Provided by: e-Prints Soton

Suggested articles


  1. (2006). ( 2009a), Support vector machines for credit scoring and discovery of significant features , Expert Systems with Applications 36,3302-3308 Bellotti T., Crook J.N. ( 2009b). Credit scoring with macroeconomic variables using survival analysis. doi
  2. (2009a), Consumer Credit Models, Pricing, Profit and Portfolios, doi
  3. (1981). A linear programming approach to the discriminant problem, doi
  4. (1970). A nonparametric approach to credit screening, doi
  5. (2000). A survey of credit and behavioural scoring; Forecasting financial risk of lending to consumers. doi
  6. (2005). A survey of the issues in consumer credit modelling research. doi
  7. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications. doi
  8. (1999). Adverse selection in the credit card market , Working Paper,
  9. (2004). Bayesian Network Classifiers for identifying the slope of the customer lifecycle of long-life customers, doi
  10. (1993). C4.5: Programs for Machine Learning, Morgan Kaufman, doi
  11. (1984). Classification and regression trees, doi
  12. (2007). Classification with Ant Colony Optimization. doi
  13. (1997). Construction of a k-nearest neighbour credit scoring system. doi
  14. (2006). Consumer Acceptance Probabilities, Expert Systems and their doi
  15. (1998). Credit Risk Modeling, Design and Application, Fitzroy Dearborn publishers,
  16. (2002). Credit Scoring and its Applications, doi
  17. (2004). Credit Scoring for Risk Managers, The Handbook for Lenders,
  18. (2002). Differentiating between good credits and bad credits using neuro-fuzzy systems. doi
  19. (2002). Direct versus indirect credit scoring classifications. doi
  20. (1986). Evaluating alternative linear programming models to solve the two-group discriminant problem, doi
  21. (2009). Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers, Expert Systems with doi
  22. (1998). Incorporating Economic Information into Credit Risk Underwriting in
  23. (2007). It's the Economy Stupid: Comparison of Proportional Hazards Models with Economic and Sociodemographic variables for estimating the purchase of financial products.
  24. (2001). Lookahead scorecards for new fixed term credit products. doi
  25. (2005). Loss Given Default validation, Studies on the validation of Internal rating systems, pp60-76, Working Paper 14, Basel Committee on Banking Supervision,
  26. (2004). Lucas A, doi
  27. (2008). Modelling credit risk of portfolio of consumer loans, to appear in doi
  28. (2009). Modelling the Credit Risk for Portfolios of Consumer Loans: Analogies with corporate loan models ,Mathematics and Computers in doi
  29. (1999). Not if but when borrowers default, doi
  30. (2007). Predicting Customer loyalty using the Internal transactional Database, Expert Systems with doi
  31. (2001). Predicting Customer Potential Value: an application in the insurance industry, Research Paper ERS-2001-01-MKT Revision_, Erasmus Research Institute of Management (ERIM)
  32. (2005). Pricing and Revenue Optimization, doi
  33. (2001). Random Forests, doi
  34. (2005). Recovery Risk, Risk Books, London Anderson R, doi
  35. (1972). Regression models and life tables ( with discussion), doi
  36. (1999). Resource pricing and the evolution of congestion control, doi
  37. (2008). Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring. Rule Extraction from Support Vector Machines, doi
  38. (2007). scoring with a data mining approach based on support vector machines, Expert Systems with Applications,.33.,847-856.. doi
  39. (2008). Stress testing retail loan portfolios with dual-time dynamics.
  40. (1992). The Flat Maximum Effect and Generic Linear Scoring Models: A Test, doi
  41. (2008). The relationship between default and economic cycle for retail portfolios across countries.
  42. (2009). Transition matrix models for consumer credit ratings, Working Paper , CORMSIS, doi
  43. (2008). Understanding the Subprime Mortgage Crisis, Working paper (2008), available at the Social Science Research Network. See abstract=1020396. 26 doi
  44. (2003). Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation. doi
  45. Vanthienen (2003b). Benchmarking state-of-the-art classification algorithms for credit scoring. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.