Satia Nur Maharani, Bambang Sugeng, Makaryanawati, Mohammad Mahbubi Ali
Financial systems depend on financial institution stability. This study used an ANFIS-ANN model to forecast bank insolvency probability. ANFIS, an adaptive network that combines fuzzy logic with ANN, is a promising bankruptcy prediction tool. This study compared the predictive performance of ANFIS against Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) using financial statements data from 42 banking issuers listed on the Indonesia Stock Exchange (IDX) from 2010 to 2021. This work aimed to build a robust bankruptcy prediction model, compared ANFIS to LSTM and CNN, and assessed each method's accuracy and applicability. MAPE and RMSE metrics were used for a thorough review. The study collected, preprocessed, and trained ANFIS, LSTM, and CNN models. ANFIS had 243 rules with triangular membership functions for input variables and constant output membership. Random search optimized LSTM and CNN hyperparameters. This study's novelty lies in its innovative comparison of ANFIS and deep learning for bank bankruptcy prediction used real-world financial data. ANFIS beat LSTM and CNN in MAPE, showing its predictive model capability. ANFIS has the lowest MAPE of 0.140335507 when trained and tested on 80%:20% data. In conclusion, ANFIS outperformed deep learning in bank soundness prediction. It provided vital insights into the banking sector's early warning and risk assessment. The results showed that ANFIS can help financial decision-makers to improve risk management. © 2023, Bright Publisher. All rights reserved.
Department of Accounting, Universitas Negeri Malang, Jl. Semarang no. 5, Malang, 65145, Indonesia; International Institute of Advanced Islamic Studies (IAIS), Malaysia
