IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

Adaptive System Of The Creditworthiness Evaluation Constructed On The Basis Of Artificial Neural Networks

Listed author(s):
  • Dorota Witkowska

    (Technical University of Lodz)

Registered author(s):

    Artificial neural networks are nonlinear models whose parameters (weights) are estimated during so called training procedure. Applying the back propagation algorithm (which is the most popular method of supervised learning), the computed output (for the initial, usually randomly chosen weights) is compared to the known output. If the generated output is correct, then nothing more is necessary. If the computed output is incorrect, then the weights are adjusted so as to make the computed output closer to the known output. Training procedure runs in a certain (usually determined by the ANN constructor) number of iterations. In many applications we deal with the objective (cost - criterion) function with many local and global minima. From the practical point of view we are interested in the localization of all global minima. The global optimization methods that have been developing rapidly in the last years include genetic algorithms which appeared to be universal, flexible and efficient. Genetic algorithms (GA) are modern successors of Monte Carlo search methods, and they belong to the class of stochastic optimization algorithms. GA are usually a compromise between searching the whole set of feasible solutions and local optimization. But with high probability they lead to the global minimum. The aim of this paper is to construct a classification system, based on neural networks, of clients of the financial institutions. This system is to recognize clients automatically thanks to the experience stored in the training set.

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below under "Related research" whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 230.

    in new window

    Date of creation: 05 Jul 2000
    Handle: RePEc:sce:scecf0:230
    Contact details of provider: Postal:
    CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain

    Fax: +34 93 542 17 46
    Web page:

    More information through EDIRC

    No references listed on IDEAS
    You can help add them by filling out this form.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:sce:scecf0:230. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.