Design of a corporate financial crisis prediction model based on improved ABC-RNN+Bi-LSTM algorithm in the context of sustainable development

Abstract

In the context of sustainable economic development, while economic globalization brings new vitality to the company, it also makes the company face an increasingly severe external environment. The managers have to shift their focus to capital market investment. The excessive pursuit of investment benefits can easily lead to decision-making errors, resulting in a financial crisis for the company, and even may be forced to delist in severe cases. This article proposes a financial crisis prediction model based on Artificial Bee Colony—recurrent neural network (ABC-RNN) and bidirectional long short-term memory (Bi-LSTM) company with a characteristic attention mechanism. We combined ABC-RNN with Bi-LSTM to extract more temporal feature vectors from financial data. Then we introduced a feature attention mechanism to extract better depth features from financial data; the ABC algorithm is introduced to optimize the weight and bias of RNN to improve the reasoning speed and accuracy. The experiment shows that the prediction accuracy and recall of the model on the test set have reached 88.94% and 88.23%, respectively, which has good prediction ability. The outcome of this research helps the company to prevent and deal with the financial crisis in time and promote the sustainable development of the market economy.

Finsformer: A Novel Approach to Detecting Financial Attacks Using Transformer and Cluster-Attention

“…In the field of financial attack detection, deep learning technologies have gained widespread attention for their excellent feature extraction capabilities and strong pattern recognition performance [1]. Convolutional neural networks (CNNs) [36,37], recurrent neural networks (RNNs) [38], and autoencoders [39] are three core deep learning models that have shown remarkable abilities in handling complex financial data. The following sections detail the structural features of these models and their applications in financial attack detection scenarios.…”

“…A recurrent neural network (RNN) is another deep learning model, particularly suited for processing sequential data [38,40]. In financial attack detection, RNNs are capable of handling the temporal dependencies of transaction data, identifying potential anomalous transaction patterns.…”

“…In the experimental section of this article, the Finsformer model was compared with traditional deep learning models such as RNN [38], LSTM [41], Transformer [39], and BERT [46]. The selection of these models aimed to comprehensively assess the performance and innovative aspects of Finsformer in the domain of financial attack detection.…”