Financial Early Warning System Model Combining Hybrid Semantic Hierarchy with Group Method of Data Handling Neural Network for Detection of Banks’ Risks

Guangju Li,Ahmed Farouk

doi:10.1155/2021/8607667

Abstract

Banks, financial, and credit institutions encountering the weakening financial system and increased risk factors cause high inflation and great losses for an economy. Detecting financial risks in advance could help financial institutions avoid losses, and the financial system could be eventually affected less. Early warning systems for banks could be helpful to identify financial risks and take measures to deal with hazardous situations. Various approaches have already been put forward. However, inaccuracy issues in risk detection are one of the main issues. Combining semantic hierarchy with the GMDH neural network to predict financial risks is proposed. A semantic hierarchy approach based on converting risk-related values and picking influential variables could be practical in risk detection. Besides, the GMDH algorithm utilizing neural networks based on available data has the capability of predicting possible risks that could occur in the future. The outcomes of the proposed method when compared to non-data mining methods suggest that it improves accuracy by almost 20%.

Highlights

With the globalization of economies and intensifying competition among banks, profit margins decreased and the risk factors increased [1]
Weaknesses in the banking system could occur due to possible reasons of boycotts imposed by the international community towards banking system, reduced government support, crisis risk, losses from transactions, exchange rate fluctuations and debt, increased market uncertainty, excessive asset holdings, cash balances on balance sheets, rising bankruptcy trends of large credit companies, and innovations of monetary and financial instruments [2]
E current financial crisis has fueled the initiatives of policymakers to construct alert systems to predict financial crises in advance, assuming that these systems could function and provide signals based on models and forecasts utilizing some indicators before a crisis could occur. e past crises proved to be very costly for both advanced and emerging economies [5]

Summary

Introduction

With the globalization of economies and intensifying competition among banks, profit margins decreased and the risk factors increased [1]. Weaknesses in the banking system could occur due to possible reasons of boycotts imposed by the international community towards banking system, reduced government support, crisis risk, losses from transactions, exchange rate fluctuations and debt, increased market uncertainty, excessive asset holdings, cash balances on balance sheets, rising bankruptcy trends of large credit companies, and innovations of monetary and financial instruments [2]. Both risk management and forecasting play significant roles. Due to the issues that existed in the previous methods, this manuscript proposes a novel method combining the GMDH deep neural network with semantic technique. erefore, the proposed method is utilized to generate a financial alert system. e rest of the paper is organized as follows: Section 2 summarizes various models dealing with financial warning systems and risk-based management. e GMDH neural network is presented in Section 3. e proposed method is introduced in Section 4. e results are presented in Section 5, and Section 6 presents the conclusion

Financial Warning Systems

GMDH Neural Network

Results

Evaluation of Results with a Semantic Technique

90 Recall