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Using the Artificial Neural Network for Credit Risk Management

#artificialintelligenceFeb-2-2019, 17:34:05 GMT

Input: The input layer is composed of neurons, taking credit risk measurement indicators as the input vector. Score values of the qualitative indicators can be obtained with the help of expert knowledge. Divided by the highest score value, the obtained score values of the indicators should be converted to the values in the range of [0, 1] for computational convenience of the ANN model. Hidden: Low-level features from the raw input data are abstracted into high-level features through multiple hidden layers. Output: There is only 1 neuron in the output layer, representing the credit risk level.

artificial intelligence, credit risk level, machine learning, (14 more...)

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Industry:

Banking & Finance > Risk Management (1.00)
Banking & Finance > Credit (1.00)

Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)

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