网上支付洗钱:反洗钱领域的新挑战

面对着日益强大的反洗钱网,洗钱者积极运用高科技手段创新洗钱方式,其中利用网上支付洗钱就是一种新型的更为隐蔽的洗钱方式.本文对利用网上支付洗钱的特点和流程进行分析和阐述,并针对我国目前网上支付发展的现状,指出网上支付中存在的风险,据此提出我国打击网上支付洗钱犯罪的对策建议,包括加强网上交易主体的身份确认,建立健全网上支付的法律法规,发展网上反洗钱监管技术等等.     

反洗钱：可疑交易行为报告制度有效吗

反洗钱可疑交易行为报告制度的实施存在监管当局与被监管者的目标冲突、金融机构认识不一、报告的边际信息价值随报告数量增加而递减、制度安排缺乏成本效益性等问题。其主要原因是制度假定忽略了金融机构与监管当局的合作矛盾，掩饰了看似合法的非法交易的存在，高估了金融机构对客户行为的控制能力和监管与披露对洗钱活动的抑制作用。要建立健全激励与约束机制，确保金融机构与当局进行实质性合作。     

The Path to Enhance Quality of Suspicious Transaction Reports for Anti-Money Laundering

反洗钱制度安排的一项核心内容是金融机构向反洗钱情报部门提交可疑交易报告。目前,金融机构可疑交易报告数量庞大而情报价值较低,已成为制约反洗钱工作开展的主要障碍。本文在分析可疑交易报告质量影响因素的基础上,给出了提升其质量的系统管理路径,包括完善报告制度、优化报告流程、实施风险管理、创新技术手段、拓展审计职能等。     

借房产税改革促房地产业反洗钱

《中华人民共和国反洗钱法》（以下简称《反洗钱法》）第三十五条从法律层面上明确特定非金融机构必须履行反洗钱义务,但对特定非金融机构的范围、其履行反洗钱义务和对其监督管理的具体办法,未予明确规定。房地产业无论从其交易方式、交易规模或是对国民经济影响力来看,都应成为反洗钱工作向特定非金融行业拓展的主要目标之一。在以风险为本监管原则的指引下,本文依托房地产业的时代背景和国家宏观调控的政策背景,结合当前反洗钱工作现状,借助房产税改革之机,提出构建房地产业反洗钱工作机制的可行性举措。     

Using IRS data to identify income shifting to foreign affiliates

Review of Accounting Studies - Income shifting is a significant source of tax planning for U.S. corporations. We use confidential Internal Revenue Service (IRS) data to develop a firm-year measure...     

POS机套现洗钱犯罪案件探析

近年来,国内利用POS机非法套现案频发,POS机套现已成为一种新型洗钱犯罪工具。本文通过搜集近年来破获的典型POS套现洗钱犯罪案件,深入分析了POS机洗钱犯罪的主要类型、特征、工作难点,并提出相关建议。     

论国际合作原则在控制腐败分子跨国洗钱中的运用

国际合作是现代国际法的基本原则。在控制腐败分子跨国洗钱方面，国际合作原则主要体现在经验与教训共享以及加强国际合作。腐败分子跨国洗钱严重威胁世界政治经济秩序，腐蚀着健康的国民经济体系。在控制腐败分子跨国洗钱方面，国际合作原则呈现发展趋势，我国要加强与国际合作，共同打击腐败分子跨国洗钱活动。     

Using clustering ensemble to identify banking business models

The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c‐means (which allows us to handle fuzzy clustering), self‐organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non‐core banks), as well as banks with a stable business model over time (persistent banks) and others (non‐persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.     

Continuous monitoring with machine learning and interactive data visualization: An application to a healthcare payroll process

This paper presents a framework for proactive and intelligent continuous control monitoring (CCM) that helps management gain higher assurance over business processes and alleviate information overload. We adopt a design science approach towards systematically developing CCM artifacts, including operation and internal control violation display and multidimensional anomaly detection. We illustrate the design with an implementation project whereby a CPA firm, the firm's healthcare sector client, and the research team work together to improve the assurance provided by payroll reviews. This study contributes to the CCM literature by envisioning that interactive data visualization and machine learning technologies can alleviate information overload for management in CCM. Second, we provide real-world evidence on the improvement brought to economic and behavioral aspects of the control monitoring process compared to the traditional approach. We show that emerging technologies substantially improve the efficiency and effectiveness of risk assessment, anomaly detection, and loss prevention. We also contribute to control monitoring practice by providing guidance on artifact development and application for practitioners to follow.     

Using macro-societal accounting data: Some critical afterthoughts

There is a tendency among researchers working in the societal accounting field to focus almost exclusively on conceptual exploration, design and measurement. This article suggests that it may be desirable to venture beyond the strictly theoretical and methodological domain and work towards satisfying concrete user needs. Personal experience is adduced to show that the output of the societal accounting movement falls short of providing the informational basis for management and political decision-making. Some ideas are also put forward as to possible ways of reducing the gap between the researcher/analyst and users of societal accounting data.     

Using client performance measures to identify pre-engagement factors associated with qualified audit reports in Greece

The purpose of this paper is to test the extent to which client (corporate) performance measures can be used to enhance the ability to discriminate between the choice of a qualified or unqualified (clean) audit report. Audit firms face the risk of losing the client if they issue a qualification. On the other hand, failing to qualify exposes the auditor to potential lawsuits and loss of reputation. We examined the financial statements, auditors' opinions, and financial statements notes for companies in Greece that received a qualified audit report and for those that received an unqualified audit report. We modeled the auditor's qualification using a multicriteria decision aid classification method (UTADIS—UTilités Additives Discriminates) and compared it with other multivariate statistical techniques such as discriminant and logit analysis. The qualification decision is explained by financial ratios and by nonfinancial information such as the client litigation. The developed models are accurate in classifying the total sample correctly with rates of almost 80%.     

中国信保：如何识别和控制信用风险？

自20世纪以来，伴随贸易全球化趋势的发展以及买方市场的形成，信用保险由于在很大程度上转移了买方的信用风险，逐渐在世界贸易的舞台上崭露头角并发展成各国促进出口及海外投资的重要工具。在当前全球金融危机的影响下，这一金融工具更加受到各国出口商的倚重，成为他们保障收汇的“护身符”。那么，中国信用保险公司（中国信保）又是如何识别、控制买方信用风险的？在承保、理赔、追偿等众多业务环节中控制风险的依据又是从何而来？对此，《国际融资》记者采访了中国信保副总经理梁志东.     

从合规为本到风险为本:第三方支付行业反洗钱监管原则的必然要求

本文结合《2008-2012中国反洗钱战略》,就如何在第三方网络支付行业贯彻风险为本方法,提升反洗钱监管的针对性、有效性和均衡性进行探讨,指出第三方支付行业的反洗钱监管面临从合规为本向风险为本原则的转变。     

货币跨境流动的反洗钱监管——基于跨境电子收付报告的视角

跨境电子收付报告能够为货币跨境流动的反洗钱监管提供理想的解决方案。跨境电子收付报告的类型化分析和差异性分析,能够满足金融监管部门对货币跨境流动的反洗钱监管需求,也可以为司法部门打击跨境经济犯罪提供高效的侦查、取证方式。本文认为,目前我国构建跨境电子收付报告制度的最大阻力来自于成本因素,但是可以通过实行批量文件处理、坚持最小信息量原则以及设立触发机制来解决,从而有效提高我国货币跨境流动的反洗钱监管效率。     

Money Laundering as a Crime in the Financial Sector: A New Approach to Quantitative Assessment,with an Application to Italy

This study provides an answer to the question of how much cash deposited via a financial institution can be traced back to criminal activities, by developing a new approach to measure money laundering and proposing an application to Italy. We define a model of cash in‐flows on current accounts considering, besides “dirty money” to be laundered, also the legal motivations to deposit cash and the role of the shadow economy. We find that the average amount of cash laundered in Italy is around 6% of GDP. These findings are coherent with estimates of the nonobserved economy obtained in previous studies.     

Using Google Trends to track the global interest in International Financial Reporting Standards: Evidence from big data

This study proposes a novel method for identifying international accounting differences under International Financial Reporting Standards (IFRS). Using Google Trends data extracted between January 2014 and August 2022, it creates an index, the Global IFRS/IAS Search Index (GISI), which comprises the search activities of 121 jurisdictions for 45 IFRS accounting standards. To assess its relative validity, I classify Nobes' (1983) 14 jurisdictions in addition to 20 OECD countries. The cluster analysis demonstrates that the GISI is a viable alternative for analyzing international differences under IFRS. The results indicate that incorporating big data could be beneficial for examining global accounting issues. A judgmental international classification of financial reporting practices     

The joint response of stock and foreign exchange markets to macroeconomic surprises: Using US and Japanese data

This paper investigates the joint response of stock and foreign exchange (FX) market returns to macroeconomic surprises, employing a system method of estimation that allows for the cross-country and cross-market interaction for asset returns and risk premia. Using US and Japanese data, we find that US stock markets are asymmetrically responsive to domestic developments in output growth and interest rates but are not influenced by macroeconomic surprises from Japan. The surprise in the FX market seems to affect stock markets in the US and Japan, respectively. In particular, we find that the interest rate surprise in the US and inflation surprise in Japan tend to overstate the impact that these surprises would have on the respective stock market. The impact of the surprises would appear smaller if macroeconomic developments induced by the FX market were incorporated into the model.