English to Chinese: finance regulation
General field: Bus/Financial
Detailed field: Finance (general) | |
Source text - English
Anti-money Laundering Compliance
At the core of any financial institution’s anti-money laundering (AML) compliance department is its transaction monitoring system. The system analyzes transactional data against preset rules and generates a list of alerts for users to review. Alerts may be reviewed on a daily, weekly, or monthly basis—but how often are the rules that generate those alerts reviewed?
For many organizations, the only formal review of the rules may occur when the vendor is configuring and installing the system. However, transaction monitoring systems are not simply “set it and forget it” solutions. As the risk appetites of financial institutions change, new customers are added and customer behavior adapts to a changing world, configurations that made sense when the system was installed might not make as much sense now. Useless alerts may be generated in high volumes and—while they may not necessarily prohibit reviewers from identifying suspicious activity—provide unnecessary distraction. The alerts may also bring undesired scrutiny from regulators and auditors.
With limited resources both in terms of staffing and dollars, AML compliance officers must be diligent about where funds are allocated and how roles are filled. For some, the means of addressing high-alert volumes is to increase staffing resources dedicated to reviewing alerts, when a more effective solution would be to evaluate the rules causing the high-alert volumes.
To optimize threshold values, many financial institutions have elected to evaluate the threshold settings in their transaction monitoring systems and make appropriate changes. Generally, this involves a statistical analysis of transaction data and alert dispositions to identify instances where thresholds can be raised or lowered, new rules may be needed. Redundant or non-productive rules can be disabled. This endeavor requires individuals with strong statistical, modeling and systems. | Translation - Chinese
金融机构反洗钱合规部门的重点在于其交易监管系统。该系统根据预设规则分析金融数据，生成警报列表供用户查阅。用户可能会以每天、每周或者每月为周期查阅警报，但这些生成警报预设规则的查阅频率究竟如何呢？
许多机构只有在他们安装配置系统的时候才会正式查阅规则。但是交易监管系统并非是简单的“安装后遗忘”的解决方法。一旦金融机构的风险承受能力发生变化，新的客户增加后其行为要适应正在变化的世界，原本安装系统有效的配置现在就不再那么有效了。系统可能会生成大量无用警报，当警报不能有效帮助查阅者辨别怀疑行为时，不必要的干扰就会发生。这些警报可能也会带来监管和审计人员的非必要审查。
因为人员和资金资源并不充裕，反洗钱合规人员就必须合理使用筹集的资金和可安排的人员。有些时候，处理高警报量的问题会采用提高查阅警报人员数量的方法，其实更有效的方法是评估产生高警报量的规则。
为了优化其最高警报量值，许多金融机构选择评估其交易监管系统的最高警报值设置并做出相应调整。一般的调整包括交易数据的统计分析以及警报的设置最高警报值升高或降低的情况确认，可能需制定新的规则，废除冗余和无效的规则。做到这些需要强大的统计、建模和系统背景完成初步分析，进而做出抉择。 |
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