Purpose

This research aims to examine the impact of money laundering (ML) and corruption on the asset quality of conventional and Islamic banks.

Design/methodology/approach

The current study used the data of conventional and Islamic banks of Pakistan from 2012 to 2018. In this study, we used fully modified ordinary least squares, dynamic ordinary least squares and pooled ordinary least square methods to analyze the data.

Findings

The results found that corruption and ML positively affect the conventional banking non-performing loans (NPLs). In contrast, corruption and ML harm the Islamic bank’s loan portfolio quality.

Originality/value

To the best of the authors’ knowledge, the relationship between corruption, ML and NPLs in conventional and Islamic banks of Pakistan are examined for the first time.

Practical Implications

According to the study’s findings, bank authorities should establish an effective method for monitoring loan activities and developing new and innovative products in Islamic banks. Additionally, the Pakistani government needs to improve anti-corruption and anti-ML policies to earn investors’ trust.

中文翻译：

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洗钱 (ML) 如何影响伊斯兰银行的贷款组合质量？

目的

本研究旨在研究洗钱 (ML) 和腐败对传统银行和伊斯兰银行资产质量的影响。

设计/方法/方法

本研究使用了2012年至2018年巴基斯坦传统银行和伊斯兰银行的数据。在本研究中，我们使用完全修正的普通最小二乘法、动态普通最小二乘法和合并普通最小二乘法对数据进行分析。

发现

结果发现，腐败和机器学习对传统银行不良贷款（NPLs）有积极影响。相比之下，腐败和机器学习损害了伊斯兰银行的贷款组合质量。

原创性/价值

据作者所知，首次研究了巴基斯坦传统银行和伊斯兰银行的腐败、洗钱和不良贷款之间的关系。

实际影响

根据研究结果，银行当局应建立有效的方法来监测伊斯兰银行的贷款活动和开发新的创新产品。此外，巴基斯坦政府需要改进反腐败和反洗钱政策，以赢得投资者的信任。