A machine learning-based early warning system for systemic banking crises

Wang, Tongyu; Zhao, Shangmei; Zhu, Guangxiang; Zheng, Haitao

doi:10.1080/00036846.2020.1870657

Cited by 34 publications

(16 citation statements)

References 36 publications

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“…Basic Framework. e construction of the risk assessment system of local government debt should be based on the analysis of the government debt situation, and the results of the risk assessment should reflect the risk level of local government debt and help the local government find and resolve risks in time [12]. A scientific and reasonable risk assessment system should carry out data collection, risk identification, risk judgment, assessment result feedback, risk monitoring, and other links on the basis of sensitivity, practicability, and pertinence.…”

Section: Construction Of Government Debt Riskmentioning

confidence: 99%

[Retracted] Risk Assessment of Government Debt Based on Machine Learning Algorithm

Chen

2021

Complexity

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Government debt risk is an important factor affecting macroeconomic stability and public expectation. The key to its prevention and control lies in early warning and early prevention. This paper builds an effective government debt risk assessment system based on machine learning algorithm. According to forming the performance of local government debt risk and its internal and external influencing factors, this study applies the analytic hierarchy process, entropy method, and BP neural network method to construct the local government risk assessment index system, which includes the primary and secondary indexes including the explicit debt risk, the contingent implicit debt risk, and the financial and economic operation risk. Using this system, this study carries on the government debt risk comprehensive weight assignment, the fiscal revenue forecast, the default probability calculation, the safety scale forecast, and finally the government debt risk assessment of the validity analysis. The system can provide signal guidance and policy reference for finance to cope with risks in advance, arrange the priority order of debt repayment, optimize the structure of fiscal revenue and expenditure, etc.

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Section: Construction Of Government Debt Riskmentioning

confidence: 99%

[Retracted] Risk Assessment of Government Debt Based on Machine Learning Algorithm

Chen

2021

Complexity

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“…From a technical perspective, this work reinforces the choice of XGBoost for classification problems using structured data. A recent study by Wang et al (2021) deconstructs the use of logit as the base classifier for EWS developed to predict banking crisis. In fact, the authors use random forest classifier to simulate expert decision, obtaining a generalisation capability above 80% area under the curve (AUC).…”

Section: -2021mentioning

confidence: 99%

Machine Learning Applied to Banking Supervision a Literature Review

Guerra

Castelli

2021

Risks

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Machine learning (ML) has revolutionised data analysis over the past decade. Like innumerous other industries heavily reliant on accurate information, banking supervision stands to benefit greatly from this technological advance. The objective of this review is to provide a comprehensive walk-through of how the most common ML techniques have been applied to risk assessment in banking, focusing on a supervisory perspective. We searched Google Scholar, Springer Link, and ScienceDirect databases for articles including the search terms “machine learning” and (“bank” or “banking” or “supervision”). No language, date, or Journal filter was applied. Papers were then screened and selected according to their relevance. The final article base consisted of 41 papers and 2 book chapters, 53% of which were published in the top quartile journals in their field. Results are presented in a timeline according to the publication date and categorised by time slots. Credit risk assessment and stress testing are highlighted topics as well as other risk perspectives, with some references to ML application surveys. The most relevant ML techniques encompass k-nearest neighbours (KNN), support vector machines (SVM), tree-based models, ensembles, boosting techniques, and artificial neural networks (ANN). Recent trends include developing early warning systems (EWS) for bankruptcy and refining stress testing. One limitation of this study is the paucity of contributions using supervisory data, which justifies the need for additional investigation in this field. However, there is increasing evidence that ML techniques can enhance data analysis and decision making in the banking industry.

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“…e irregular wood classifier taking into account experts' voting process is the most effective among AI classifiers. e experts' voting EWS orchestrating multivariate data could be more appropriate to provide alarms when shifted settings exist [19].…”

Section: Financial Warning Systemsmentioning

confidence: 99%

“…e GMDH neural network performs better than Single Exponential Smoothing, Double Exponential Smoothing, ARIMA, and Open Neural Network. e inductive method, which sorts gradually complex polynomial models and selects the best solution with external criterion, is employed in the GMDH algorithms [19].…”

Section: Gmdh Deep Neural Networkmentioning

confidence: 99%

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

2021

Discrete Dynamics in Nature and Society

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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%.

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A machine learning-based early warning system for systemic banking crises

Cited by 34 publications

References 36 publications

[Retracted] Risk Assessment of Government Debt Based on Machine Learning Algorithm

[Retracted] Risk Assessment of Government Debt Based on Machine Learning Algorithm

Machine Learning Applied to Banking Supervision a Literature Review

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

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