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Middle East banks embrace ESG strategies

27 February 2023

Financial institutions in the Middle East and North Africa (Mena) are scaling up environmental, social and governance (ESG) principles in their business models, according to insights from US-based consultancy Arthur D. Little.

This is evidenced by a growing number of banks moving from defining ESG strategies towards implementation.

The region recorded $24.55bn worth of green and sustainable finance issuances in 2021, a 532 per cent year-on-year increase compared to $3.8bn raised in 2020.

Notable transactions included a $3bn green loan issued by Egypt; Etihad Airways raising $1.2bn through a sustainability-linked loan in October; and a $100m revolving green loan signed by Masdar in December.

The tail end of 2022 saw $1.25bn raised by retail conglomerate Majid al-Futtaim (MAF). Led by First Abu Dhabi Bank, the revolving credit facility is linked to MAF's ESG goals, such as reducing scope 1 and 2 emissions; implementing LEED certification for its buildings; and improving gender diversity.

Meanwhile, at the outset of 2023, Qatar said that it was eyeing $75bn worth of investments in sustainable finance.

Institutions, however, find that the complexity of ESG data has not been entirely captured and addressed by current data governance frameworks, leaving these banks to resort to ad hoc solutions for collecting, managing and governing ESG data.

"Banks in the Middle East have embraced the importance of a well-defined ESG strategy," says Nael Amin, senior manager, financial services practice at Arthur D. Little. 

"During the next step, implementation and frameworks such as data governance are vitally necessary. The shift from strategy to implementation is complex and detail-oriented. Different use cases of ESG have different data requirements and multiple stakeholders who add to the complexity. Thus, there is no standard “one size fits all” in regard to ESG data.”

Challenges facing banks on the path to implementing ESG-led strategies:

1. Dynamic ESG data requirements

2. Lack of data availability and transparency

3. Inconsistent data quality

Related reads:

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Mehak Srivastava

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