| Title: |
IMPROVING SCORING TECHNIQUES IN BANKING APPLICATIONS. |
| Authors: |
Perrodo, Pascal |
| Source: |
Applied Stochastic Models & Data Analysis; Sep93, Vol. 9 Issue 3, p231-244, 14p |
| Subject Terms: |
Orthogonal polynomials; Artificial neural networks; Marketing; Banking industry; Risk assessment; Risk management in business |
| Abstract: |
Scoring techniques are largely used today in banking applications, for marketing as well as risk assessment or control purposes. The most widely used method is Fisher's discriminatory analysis. We discuss here this method from a technical standpoint, showing its weaknesses and then propose a simpler and more general alternative. We first make an original presentation of the various scoring techniques and their basic principles. In particular, we provide a detailed description of Fisher's method, insisting on sensitive steps such as the discretization of continuous variables which is responsible for the nonlinearity of the model. We then go on to propose a new method that is completely linear and improves scoring performance. This technique is easy to explain, compute and use in real applications. Finally, we compare the performance of this new method with that of traditional techniques and of the more up-to-date neural nets method. [ABSTRACT FROM AUTHOR] |
| : |
Copyright of Applied Stochastic Models & Data Analysis is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |