An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking

Liquidity risk represent a devastating financial threat to banks and may lead to irrecoverable consequences in case of underestimation or negligence. The optimal control of a phenomenon such as liquidity risk requires a precise measurement method. However, liquidity risk is complicated and providing a suitable definition for it constitutes a serious obstacle. In addition, the problem of defining the related determining factors and formulating an appropriate functional form to approximate and predict its value is a difficult and complex task. To deal with these issues, we propose a model that uses Artificial Neural Networks and Bayesian Networks. The implementation of these two intelligent systems comprises several algorithms and tests for validating the proposed model. A real-world case study is presented to demonstrate applicability and exhibit the efficiency, accuracy and flexibility of data mining methods when modeling ambiguous occurrences related to bank liquidity risk measurement. (C) 2017 Elsevier B.V. All rights reserved.