Article2023

Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest Based FinTech Application for Hyper-automation

Subramani Neelakandan · Abbas Mardani · Sudhanshu Maurya · N. Arulkumar · K. Thangaraj

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Financial Distress and Bankruptcy Prediction Metaheuristic Optimization Algorithms Research Stock Market Forecasting Methods

Abstract

Hyper automation is the group of approaches and software companies utilised to automate manual procedures. Financial Technology (FinTech) was processed as a distinctive classification that highly inspects the financial technology sector from a broader group of functions for enterprises with utilise of Information Technology (IT) application. Financial crisis prediction (FCP) is the most essential FinTech technique, defining institutions’ financial status. This study proposes an Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest (ESAOA-ODCF) based FinTech Application for Hyperautomation. The ESAOA-ODCF technique has achieved exceptional performance with maximum accu y of 98.61%, and F score of 98.59%. Extensive experimental research revealed that the ESAOA-ODCF model beat more modern, cutting-edge approaches in terms of overall performance.

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References

2017Deep learning with long short-term memory networks for financial market predictions 2018ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module 2018Bankruptcy prediction using imaged financial ratios and convolutional neural networks 2020Deep learning in finance and banking: A literature review and classification 2020A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM 2020Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE 2019An overview of bankruptcy prediction models for corporate firms: A Systematic literature review 2019Predicting distresses using deep learning of text segments in annual reports

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