Use of Artificial Neural Network in Predicting Financial Distress in Banking Companies in Indonesia
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Abstract
A situation known as .financial distress. occurs when the company's finances are in poor or experiencing a decline before bankruptcy occurs. To test and predict transport, you can use an Artificial Neural Network. The purpose aims to predict Banks .that are listed on the. Indonesian stock exchange. are experiencing financial difficulties using predictive indicators, namely the current .ratio, .return on equity, .and BOPO. Based on the research results of the summary model in neural network testing, the Artificial Neural Network backpropagation algorithm was applied to obtain 3-2-2 architecture results. Based on the results of the three tests, the percent incorrect prediction value was obtained, which had the lowest error rate, namely in the first test, namely 11.7% or a model correctness level of 88.3% in determining the neural network. The highest influence on financial distress is the Current Ratio variable, with an importance value of 0.521. Based on three tests, it is stated that the prediction model using the artificial neural network algorithm backpropagation indicators CR, ROE, and BOPO can predict financial distress. CR is the most dominant influencer in financial distress. The outcome of this research have implications for banking companies that need to pay attention to or maintain liquidity levels because this proportion shows how much money the organization has plus assets that can turn into money in the short term.
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