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BiLSTM OptiFlow: an enhanced LSTM model for cooperative financial health forecasting

Year: 2025

Type: article

Abstract: This paper presents bidirectional long short-term memory (BiLSTM) OptiFlow, an optimized deep learning model designed to predict the financial health of cooperatives using key financial ratios: debt to equity ratio (DER), net profit margin (NPM), and return on equity (ROE). By leveraging a BiLSTM architecture combined with an optimal decayed learning rate, this model aims to enhance forecasting accuracy. The proposed model was tested against three established methods—recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)—and evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared error (MSE) metrics. Results indicate that BiLSTM OptiFlow outperforms the other models across all key indicators. This research offers a robust approach to cooperative financial forecasting, with significant implications for decision-making processes in cooperative management.

Source: Bulletin of Electrical Engineering and Informatics

DOI: 10.11591/eei.v14i3.8653

Authors: Evi Maria, Teguh Wahyono, Kristoko Dwi Hartomo, Purwanto Purwanto, Christian Arthur

Institutions: Satya Wacana Christian University, University of Technology Sydney

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: diamond

OpenAlex ID: W4410045591

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