Purpose

The purpose of this study is to explore and use artificial intelligence (AI) techniques for identifying the relevant attributes necessary to file a suspicious activity report (SAR) using historical customer transactions. This method is known as predictive modeling, a statistical approach which uses machine learning algorithm to predict outcomes by using historical data. The models are applied to a modified data set designed to mimic transactions of retail banking within the USA.

Design/methodology/approach

Machine learning classifiers, as a subset of AI, are trained using transactions that meet or exceed the minimum threshold amount that could generate an alert and report a SAR to the government authorities. The predictive models are developed to use customer transactional data to predict the probability that a transaction is reportable.

Findings

The performance of the machine learning classifiers is determined in terms of accuracy, misclassification, true positive rate, false positive rate and false negative rate. The decision tree model provided insight in terms of the attributes relevant for SAR filing based on the rule-based criteria of the algorithm.

Originality/value

This research is part of emerging studies in the field of compliance where AI/machine learning technology is used for transaction monitoring to identify relevant attributes for suspicious activity reporting. The research methodology may be replicated by other researchers, Bank Secrecy Act/anti-money laundering (BSA/AML) officers and model validation analysts for BSA/AML compliance models.

中文翻译：

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使用预测模型来识别可疑活动报告的相关特征

目的

本研究的目的是探索和使用人工智能 (AI) 技术来识别使用历史客户交易提交可疑活动报告 (SAR) 所需的相关属性。这种方法被称为预测建模，一种使用机器学习算法通过历史数据预测结果的统计方法。这些模型被应用于旨在模拟美国零售银行交易的修改数据集。

设计/方法/方法

机器学习分类器作为 AI 的一个子集，使用达到或超过可以生成警报并向政府当局报告 SAR 的最低阈值数量的交易进行训练。开发预测模型以使用客户交易数据来预测交易可报告的概率。

发现

机器学习分类器的性能由准确率、误分类率、真阳性率、假阳性率和假阴性率决定。决策树模型基于算法的基于规则的标准，提供了与 SAR 归档相关的属性方面的洞察力。

原创性/价值

这项研究是合规领域新兴研究的一部分，其中人工智能/机器学习技术用于交易监控，以识别可疑活动报告的相关属性。其他研究人员、银行保密法/反洗钱 (BSA/AML) 官员和 BSA/AML 合规模型的模型验证分析师可能会复制该研究方法。