合规指数:(后)新冠肺炎时代合规风险管理的行为方法

IF 0.4 4区 经济学 Q4 BUSINESS, FINANCE Journal of Operational Risk Pub Date : 2022-01-01 DOI:10.21314/jop.2022.020
S. Rick, Ralf Jasny
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引用次数: 0

摘要

2019冠状病毒病大流行及其新的混合工作模式意味着,为控制和监测合规计划的有效性而建立的传统控制系统很快就达到了自然极限。特别是,当员工在家工作时,由于法律和技术原因,减轻合规风险的正式控制(例如审计和监视程序)很难或不可能实现。因此,需要从这种控制转向行为方法的范式转变。为了应对2019冠状病毒病大流行引发的工作场所变化,我们提出了合规指数,该指数将组织合规计划的合法性置于这些考虑的中心。合规指数是一种基于经验的行为测量系统,用于控制和监测合规计划的有效性,以减轻合规风险。我们讨论了合规指数的性质和目的,并解释了该模型的理论基础和用于估计模型参数的定量方法。我们发现,以发现不当行为、追究投诉或惩罚违规行为为重点的道德领导和合规计划,对合规计划的合法性有积极影响;合规计划的合法性对员工举报违规行为的意愿有积极影响;合规计划的合法性对合规风险有消极影响。最后,我们讨论了合规指数对从业人员工作的影响,并强调了进一步研究的方法。©2022 Infopro Digital Risk (IP) Limited。
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The Compliance Index: a behavioral approach to compliance risk management in the (post-) Covid-19 era
The Covid-19 pandemic, with its new hybrid work models, has meant that conventional control systems set up for controlling and monitoring the effectiveness of compliance programs quickly reached their natural limits. In particular, when employees work from home, formal controls (such as auditing and monitoring procedures) to mitigate compliance risk are difficult or impossible to implement for legal and technical reasons. Thus, a paradigm shift away from such controls to a behavioral approach is required. To address the changes in the workplace triggered by the Covid-19 pandemic, we present the Compliance Index, which places the legitimacy of compliance programs in organizations at the center of these considerations. The Compliance Index is an empirically based behavioral measurement system for controlling and monitoring the effectiveness of compliance programs to mitigate compliance risk. We discuss the nature and purpose of the Compliance Index and explain the theory underlying the model and the quantitative approach employed to estimate the model parameters. We find that ethical leadership and compliance programs that focus, for example, on detecting misconduct, pursuing complaints or penalizing rule violations have a positive effect on the legitimacy of compliance programs;the legitimacy of compliance programs has a positive effect on employees’ willingness to report violations;and the legitimacy of compliance programs has a negative effect on compliance risk. Finally, we discuss the impact of the Compliance Index on the work of practitioners and highlight approaches for further research. © 2022 Infopro Digital Risk (IP) Limited.
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来源期刊
Journal of Operational Risk
Journal of Operational Risk BUSINESS, FINANCE-
CiteScore
1.00
自引率
40.00%
发文量
6
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.
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