Retooling the bank for sustainable lending

Sustainable lending, like sustainable investing, means that environmental, social, and governance (ESG) considerations play a central role in credit decisions. A sustainability-linked loan will typically focus on giving the borrower incentives to meet ESG performance objectives. The conditions of the loan will be tied to the achievement of ESG metrics such as carbon emissions or workforce and board diversity.

In the wake of the COVID-19 health and financial crisis, banks face growing calls to play their part in addressing today’s environmental and social concerns. Some of the forces driving the rise of sustainable lending include:

• Regulators are focusing on the disclosure of sustainability and ESG policies, including loans to projects and organizations that record high greenhouse gas emissions.
• Investors are scrutinizing how banks are addressing issues such as climate change and diversity, including how their loan books take into account ESG considerations.
• Customers are looking to banks to do the right things.
• Growing credit risks—sustainable lending laggards may face an increase in credit risks and a decline in profitability as ESG risks become concentrated in their loan books.
• Leading banks see this as an opportunity to strengthen public trust.

• Green mortgages offer houseowners a discount on the mortgage rate if the house meets specific energy standards.
• Sustainability-linked loans or revolving credit facilities give borrowers discounted interest rates for achieving ESG goals or benchmarks.
• Green loans promote a greener economy, facilitating investments in renewable energy, green buildings and sustainable farming.
• Sustainability-linked supply chain finance gives suppliers preferential rates if they meet sustainability-linked metrics.
• Green loans securitization—asset-backed security with proceeds raised to finance loans for green infrastructure.

A medium-term ESG roadmap might focus on these steps:

• Banks may adjust credit products, policies and processes to reflect ESG principles. Product specifications, documentation and collaterals or covenants will need to be adapted, while lending committees will consider ESG information and profiles in lending decisions.
• Relationship managers will need to be equipped with training and industry knowledge so they can make financial decisions based on both ESG and industry considerations.
• Leading banks will build on their progress in automating and speeding up credit processing. They will embed the same digital standards used in the rest of the lending business into sustainable lending operations.

Many banks’ credit professionals have not been trained to assess lending terms and conditions through the lens of sustainability, nor do they have skills in analyzing ESG data. We suggest three actions to consider:

• Banks could offer standardized, continuous and modular training, comprising a mixture of interactive in-person and online sessions. With a modular approach, banks can train all team members to the same baseline of knowledge, while offering specialist modules for different roles.
• Allowing employees to earn badges or certifications for completing modules will make it easy for leaders and colleagues to track who has been trained and in which topics.
• An ESG forum or committee can be established to formalize the sharing of lessons learned and best practices.

We recommend these possible steps:

• Banks can select third-party ESG score methodologies for assessing listed companies, or develop their own ESG methodology and certify it with an independent institution. They could also blend these approaches.
• For unlisted companies and those not rated by independent agencies, banks can gather ESG information during interviews or via online questionnaires.
• To facilitate accessing, validating and managing internal and externally sourced ESG data, banks can establish an ESG central data utility. This structure will take care of data management, definition of data policies and data frameworks, and running ESG data operations.