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. © 2017 Elsevier B.V. All rights reserved