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Anti-money laundering disclosures and banks’ performance

Author

Listed:
  • Haitham Nobanee
  • Nejla Ellili

Abstract

Purpose - The purpose of this paper is to explore the extent of anti-Money laundering (AML) disclosures in the annual reports and websites by differentiating between UAE Islamic and conventional banks, and examine the effect of AML disclosure on UAE bank’s performance. Design/methodology/approach - This study uses content analysis to explore the extent of AML disclosure in the annual reports and the dynamic panel data two-step robust system to study the impact of the AML disclosures on banking performance. Findings - The findings show that AML disclosure is at a low level for all UAE banks, conventional and Islamic banks. The results also show that the degree of AML disclosure on the websites of the banks is higher than that in the annual reports. Research limitations/implications - The sample for this study comes only from banks traded on UAE markets. Thus, the results may not be generalizable to banks traded on other financial markets. Practical implications - Because of the cross-border character of the money laundry practices, our study suggests the UAE central bank to internationalize the AML regulations and develop an international AML regime as efforts to respond to the international development of the money laundry practices. Originality/value - This is the first study that develops an index to measure the AML disclosure and contributes significantly in providing greater insight in respect to AML disclosure in banking industry within the emerging markets.

Suggested Citation

  • Haitham Nobanee & Nejla Ellili, 2018. "Anti-money laundering disclosures and banks’ performance," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 25(1), pages 95-108, January.
  • Handle: RePEc:eme:jfcpps:jfc-10-2016-0063
    DOI: 10.1108/JFC-10-2016-0063
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    More about this item

    Keywords

    Islamic banks; Dynamic panel data; Anti-money laundering; Banking performance; Voluntary disclosures; C33; G32; G34;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance

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