Adaptive System Of The Creditworthiness Evaluation Constructed On The Basis Of Artificial Neural Networks
AbstractArtificial 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.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 230.
Date of creation: 05 Jul 2000
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