将文本挖掘和层次分析法风险评估与知识图谱相结合，进行操作风险分析

IF 0.4 4区经济学 Q4 BUSINESS, FINANCE

Journal of Operational Risk Pub Date : 2023-01-01 DOI:10.21314/jop.2023.004

Zuzhen Ji, Xuhai Duan, D. Pons, Yong Chen, Zhi Pei

引用次数: 1

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Integrating text mining and analytic hierarchy process risk assessment with knowledge graphs for operational risk analysis

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来源期刊

Journal of Operational Risk BUSINESS, FINANCE-

CiteScore

1.00

自引率

40.00%

发文量

期刊介绍： In December 2017, the Basel Committee published the final version of its standardized measurement approach (SMA) methodology, which will replace the approaches set out in Basel II (ie, the simpler standardized approaches and advanced measurement approach (AMA) that allowed use of internal models) from January 1, 2022. Independently of the Basel III rules, in order to manage and mitigate risks, they still need to be measurable by anyone. The operational risk industry needs to keep that in mind. While the purpose of the now defunct AMA was to find out the level of regulatory capital to protect a firm against operational risks, we still can – and should – use models to estimate operational risk economic capital. Without these, the task of managing and mitigating capital would be incredibly difficult. These internal models are now unshackled from regulatory requirements and can be optimized for managing the daily risks to which financial institutions are exposed. In addition, operational risk models can and should be used for stress tests and Comprehensive Capital Analysis and Review (CCAR). The Journal of Operational Risk also welcomes papers on nonfinancial risks as well as topics including, but not limited to, the following. The modeling and management of operational risk. Recent advances in techniques used to model operational risk, eg, copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory. The pricing and hedging of operational risk and/or any risk transfer techniques. Data modeling external loss data, business control factors and scenario analysis. Models used to aggregate different types of data. Causal models that link key risk indicators and macroeconomic factors to operational losses. Regulatory issues, such as Basel II or any other local regulatory issue. Enterprise risk management. Cyber risk. Big data.