Resumen o Introducción
When time and foresight permit advance arrangement of loans, the act of borrowing can be made much simpler. When time is short and the need for the loan was not anticipated, the act of going through the process of borrowing may be so time-consuming that obtaining the loan may not be possible at all.
Efforts are being made to develop expert system for analyzing credit risk in consumer loan to overcome these problems. Artificial neural networks (ANN) are used as expert system for credit risk analysis in consumer loan. Radial Basis Function (RBF), Recurrent Neural Network (RNN), and Backpropagation or Multilayer Perceptron (MLP) are the three most popular Artificial Neural Network (ANN) tools for the prediction task.
We used both feed forward neural network and radial basis function neural network, back propagation algorithm to make the credit risk prediction. The network can be trained with available data to model an arbitrary system. The trained network is then used to predict the risk in granting the loan.
- Citar trabajo
- Shilpa Laddha (Autor), 2007, Consumer Loan Credit Risk Analyser Using Neural Networks, Múnich, GRIN Verlag, https://www.grin.com/document/503709
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