Purpose

The purpose of this paper is to address the effect of corruption and money laundering (ML) on banking profitability and stability.

Design/methodology/approach

This study uses the panel data of 72 banks of Pakistan and Malaysia from 2012–2018. This paper uses fixed effect (FE) and random effect (RE) regression techniques for empirical testing and generalized methods of moment (GMM) technique for robustness tests.

Findings

This study founds consistent evidence that corruption has a positive and ML has a negative relationship with the banking profitability of Pakistan and Malaysia while the empirical evidence suggests that corruption and ML have a diverse impact on the banking stability of Pakistan and Malaysia. Further, this paper also founds that corruption and ML moderates the relationship between risk and banking profitability and stability.

Practical implications

The results reveal that the banks of the highly corrupt environment are more affected by corruption and ML than the least corrupt environment. Thus, it is recommended that the Government of Pakistan should formulate strong anti-corruption and anti-money laundering policies.

Originality/value

As per the knowledge of the authors, this research contributes to understanding the role of corruption and money laundering on the stability and profitability of Pakistan and, in general, it is the first attempt investigating the moderating role of corruption and ML between risk and banking profitability and stability.

中文翻译：

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探索腐败和洗钱 (ML) 对银行业盈利能力和稳定性的影响：对巴基斯坦和马来西亚的研究

目的

本文旨在探讨腐败和洗钱 (ML) 对银行业盈利能力和稳定性的影响。

设计/方法/方法

本研究使用了巴基斯坦和马来西亚 72 家银行 2012-2018 年的面板数据。本文使用固定效应 (FE) 和随机效应 (RE) 回归技术进行实证检验，并使用广义矩量法 (GMM) 技术进行稳健性检验。

发现

这项研究发现一致的证据表明腐败与巴基斯坦和马来西亚的银行业盈利能力呈正相关，而ML 与巴基斯坦和马来西亚的银行业盈利能力呈负相关，而经验证据表明腐败和ML对巴基斯坦和马来西亚的银行业稳定性有不同的影响。此外，本文还发现腐败和 ML 调节了风险与银行业盈利能力和稳定性之间的关系。

实际影响

结果表明，与腐败程度最低的环境相比，高度腐败环境中的银行更容易受到腐败和机器学习的影响。因此，建议巴基斯坦政府制定强有力的反腐败和反洗钱政策。

原创性/价值

根据作者的知识，这项研究有助于理解腐败和洗钱对巴基斯坦稳定和盈利能力的作用，总的来说，这是第一次尝试调查腐败和 ML 在风险和银行盈利能力之间的调节作用和稳定性。