摘要翻译：
提出了一个基于贝叶斯网络计算范式的操作风险管理系统。该算法只需利用内部损失数据就可以构建针对每个银行的贝叶斯网络，并以简单而现实的方式考虑了银行不同流程之间的相关性。在一个可变的时间范围内对内部损耗进行平均，从而消除了不同时间的相关性，同时保持了同一时间的相关性：因此平均损耗适合于执行网络拓扑和参数的学习。该算法在合成时间序列上得到了验证。需要强调的是，算法的实际实施对银行组织结构的影响很小，而且只需要在计算领域投入人力资源。
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英文标题：
《A Bayesian Networks Approach to Operational Risk》
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作者：
V. Aquaro, M. Bardoscia, R. Bellotti, A. Consiglio, F. De Carlo, G.
  Ferri
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最新提交年份：
2012
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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英文摘要：
  A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank using only internal loss data, and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters. The algorithm has been validated on synthetic time series. It should be stressed that the practical implementation of the proposed algorithm has a small impact on the organizational structure of a bank and requires an investment in human resources limited to the computational area.
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PDF链接：
https://arxiv.org/pdf/0906.3968