What to learn from near misses: An inductive learning approach to credit risk assessment

Decision Sciences

Volumn 28, Issue 1, 1997, Pages 105-120

  Tessmer, Antoinette Canart ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Decision support systems; Decision theory; Finance

Indexed keywords

EID: 0038873708     PISSN: 00117315     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1540-5915.1997.tb01304.x     Document Type: Article

References (25)

1. Bouwman, M.J. (1984). Expert vs novice decision making in accounting: A summary. Accounting Organization and Society, 9(3/4), 325-327.
2. Bouwman, M.J. (1985). Towards expert systems: The analysis of expert financial behavior. Working paper, College of Business Administration, University of Oregon.
3. Bouwman, M.J. (1987). How do financial analysts make decisions? A process model of the investment screening decision. Accounting Organizations and Society, 12(1), 1-29.
4. Braun, H., & Chandler, J.S. (1987). Predicting stock market behavior through rule-decision: An application of the learning-from-examples approach. Decision Sciences, 18(3), 415-429.
5. Breiman, L., Friedman, I.H., Olshen, R.A., & Stone, C.J. (1984). Classification and regression trees. Belmont, CA: Wadsworth International Group.
6. Carter, C., & Catlett, J. (1987). Assessing credit card applications using machine learning. IEEE Expert, Fall, 71-79.
7. Chandler, J.S., Liang, T., & Han, I. (1989). An empirical comparison of probit and ID3 methods for accounting classification research. Working paper, Department of Accountancy, College of Commerce and Business Administration, University of Illinois at Urbana-Champaign.
8. Cohen K.J., Gilmore, T.C., & Singer, F.A. (1966). Bank procedures for analyzing business loan applications. In K.J. Cohen & F.S. Hammer (Eds.), Analytical methods in banking. Homewood, IL: Richard D. Irwin.
9. Cronan, T.P., Glorfeld, L.W., & Perry, L.G. (1991). Production system development for expert systems using a recursive partitioning induction approach: An application to mortgage, commercial, and consumer lending. Decision Sciences, 22(4), 812-845.
10. Currim, I.S., Meyer, R.J., & Le, N.T. (1986). Disaggregate tree-structured modeling of consumer choice data. Working paper, Graduate School of Management, University of California, Los Angeles.
11. Deng, P.S. (1993). Automating knowledge acquisition and refinement decision support: A connectionist inductive inference model. Decision Sciences, 24(2), 371-394.
12. Elmer, P.J., & Borowski, D.M. (1988). An expert system approach to financial analysis: The case of S&L bankruptcy. Financial Management, 17(3), 66-76.
13. Han, I. (1990). The impact of measurement scale on classification performance of inductive learning and statistical approaches. Doctoral dissertation, Department of Accountancy, University of Illinois at Urbana-Champaign.
14. Hester, D.D. (1966). An empirical examination of a commercial bank loan offer function. In K.J. Cohen & F.S. Hammer (Eds), Analytical methods in banking. Homewood, IL: Richard D. Irwin.
15. Hunt, E.B., Marin, J., & Stone, P.J. (1966). Experiments in induction. New York: Academic Press.
16. Messier, W.F., & Hansen, J.V. (1988). Inducing rules for expert system development: An example using default and bankruptcy data. Management Science, 34(12), 1403-1415.
17. O'Leary, D.E. (1987). Validation of expert systems-With applications to auditing and accounting expert systems. Decision Sciences, 18(3), 468-486.
18. Rendell, L., & Cho, H. (1988). Empirical concept learning as a function of data sampling and concept character. Technical Report R-88-1410, Department of Computer Science, University of Illinois at Urbana-Champaign.
19. Salchenberger, L.M., Cinar, E.M., & Lash, N.A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23(4), 899-916.
20. Shaw, M.J., & Gentry, J.A. (1988). Using an expert system with inductive learning to evaluate business loans. Financial Management, Autumn, 45-56.
21. Shaw, M.J., & Gentry, J. A. (1990). Inductive learning for risk classification. IEEE Expert, February.
22. Smoke, K.L. (1933). Negative instances in concept leaning. Journal of Experimental Psychology, 16, 583-588.
23. Tessmer, A.C. (1992). Machine learning applied to the credit granting decision - The case of small Belgian businesses. Doctoral dissertation, University of Namur, Namur, Belgium.
24. Tessmer, A.C., Shaw, M.J., & Gentry, J.A. (1993). Inductive learning for international financial analysis: A layered approach. Journal of Management Information System, Spring, 9(4), 17-36.
25. Winston, P.H. (1975). Learning structural description from examples. The Psychology of Computer Vision. New York: McGraw-Hill.