An internal fraud model for operational losses in retail banking

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Rocío Paredes, Marco Vega
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引用次数: 0

Abstract

This article presents a novel dynamic model for internal fraud losses in the retail banking sector, incorporating internal factors such as ethical quality of workers and bank risk controls. The model's parameters are calibrated for each bank in the Operational Riskdata eXchange (ORX) consortium, based only on publicly available exposure indicators. The model generates simulated internal operational losses, exhibiting standard stochastic properties and tail behavior that closely align with actual operational losses. At an aggregate level, the model endeavors to replicate the average frequency and severity of losses observed within the internal fraud—retail banking category. Moreover, we identify macro-environmental factors that exert influence over the severity and frequency of model-simulated losses, consistent with findings in the existing literature.

零售银行业务损失的内部欺诈模型
本文提出了一个新的动态模型,为内部欺诈损失在零售银行部门,纳入内部因素,如工人的道德素质和银行风险控制。该模型的参数仅基于公开可用的风险敞口指标,针对操作风险数据交易所(ORX)联盟中的每家银行进行校准。该模型生成模拟的内部作业损失,显示出标准的随机特性和尾部行为,与实际作业损失密切相关。在总体水平上,该模型努力复制在内部欺诈零售银行类别中观察到的损失的平均频率和严重程度。此外,我们确定了影响模型模拟损失的严重程度和频率的宏观环境因素,这与现有文献的发现一致。
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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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