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Modeling of extended osprey optimization algorithm with Bayesian neural network: An application on Fintech to predict financial crisis

Ilyоs Abdullayev · Elvir Akhmetshin · Irina Vyacheslavovna Kosorukova · Elena Klochko · Woong Cho · Gyanendra Prasad Joshi

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

Abstract

Accurately predicting and anticipating financial crises becomes of paramount importance in the rapidly evolving landscape of financial technology (Fintech). There is an increasing reliance on predictive modeling and advanced analytics techniques to predict possible crises and alleviate the effects of Fintech innovations reshaping traditional financial paradigms. Financial experts and academics are focusing more on financial risk prevention and control tools based on state-of-the-art technology such as machine learning (ML), big data, and neural networks (NN). Researchers aim to prioritize and identify the most informative variables for accurate prediction models by leveraging the abilities of deep learning and feature selection (FS) techniques. This combination of techniques allows the extraction of relationships and nuanced patterns from complex financial datasets, empowering predictive models to discern subtle signals indicative of potential crises. This study developed an extended osprey optimization algorithm with a Bayesian NN to predict financial crisis (EOOABNN-PFC) technique. The EOOABNN-PFC technique uses metaheuristics and the Bayesian model to predict the presence of a financial crisis. In preprocessing, the EOOABNN-PFC technique uses a min-max scalar to scale the input data into a valid format. Besides, the EOOABNN-PFC technique applies the EOOA-based feature subset selection approach to elect the optimal feature subset, and the prediction of the financial crisis is performed using the BNN classifier. Lastly, the optimal parameter selection of the BNN model is carried out using a multi-verse optimizer (MVO). The simulation process identified that the EOOABNN-PFC technique reaches superior accuracy outcomes of 95.00% and 95.87% compared with other existing approaches under the German Credit and Australian Credit datasets.

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References

2019FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk 2023Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach 2018RETRACTED ARTICLE: Intelligent hybrid model for financial crisis prediction using machine learning techniques 2021Predicting financial crises with machine learning methods 2023Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest Based FinTech Application for Hyper-automation 2022A Network Based Fintech Inclusion Platform 2023Predicting systemic risk in financial systems using Deep Graph Learning 2021An Intelligent Internet-of-Things-Aided Financial Crisis Prediction Model in FinTech

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