Purpose

Money laundering (ML) is one of the greatest challenges, the global community faces today. Corporate entities such as financial institutions (FIs) are most susceptible to facilitate and launder money. The paper raises the following question: Who is to bear the burden of liability? Either a corporation or an individual, thus this paper examines liability issues in a corporate setting particularly financial institutions, which arise from regulatory noncompliance or failure to oversight in the context of ML.

Design/methodology/approach

The study is legal doctrinal mainly based on case laws, legislation and research articles.

Findings

Firstly, this study provides how the concept of liability in a corporate setting in UK and USA has drifted from its traditional “duty to care” standard to a new “duty to oversight” and “Responsible Corporate Officer” concepts resulting a shift in corporate to individual liability. Secondly, in the context of anti-ML violations in FIs, imposition of corporate or personal liability solely may not effectively deter ML and may create conflicts between management and shareholders.

Practical implications

The paper can be a source to explore the issue of ML liability for regulatory noncompliance based on UK, USA and Pakistan law.

Originality/value

This paper demonstrates that the imposition of either corporate or personal liability may create dilemma either for shareholders or management; however, a “combine or collective liability” approach carries potential to retard ML activities in FIs and balancing the harm-penalties incurred upon a corporation while addressing shareholders concerns.

中文翻译：

---

金融机构与反洗钱违规：谁来承担责任？

目的

洗钱 (ML) 是当今全球社会面临的最大挑战之一。金融机构 (FI) 等公司实体最容易为洗钱提供便利。论文提出以下问题：谁来承担责任？无论是公司还是个人，因此本文研究了公司环境中的责任问题，尤其是金融机构，这些问题是由于在 ML 的背景下监管不合规或未能监督而引起的。

设计/方法/方法

该研究是法律学说，主要基于判例法、立法和研究文章。

发现

首先，本研究提供了英国和美国公司环境中的责任概念如何从其传统的“注意义务”标准转变为新的“监督义务”和“负责任的公司官员”概念，从而导致公司向个人责任。其次，在金融机构违反反洗钱的情况下，仅施加公司或个人责任可能无法有效地阻止 ML，并可能在管理层和股东之间造成冲突。

实际影响

该论文可以作为探讨基于英国、美国和巴基斯坦法律的监管违规的机器学习责任问题的来源。

原创性/价值

本文表明，公司或个人责任的强加可能会给股​​东或管理层带来两难境地；然而，“联合或集体责任”方法有可能延缓金融机构中的机器学习活动，并在解决股东担忧的同时平衡公司遭受的损害惩罚。