The financial sector has been thinking about Environmental, Social, and Governance (ESG) for the last several years, primarily in the areas of climate change and racial bias in lending. But there are many more areas to explore, such as expanding product offerings that meet higher ESG standards and limiting exposure to investments that do not.
Going forward, we must go beyond talking about “green” and use data to assess transactions from end to end. There is a huge data gap that Artificial Intelligence (AI) can fill, and with the help of new analytics practices, it can become a differentiator.
Let’s face it, socially aware individuals today want assurances that the companies, lending products, and investment instruments they use are vetted in the principles of ESG. But the practicalities of managing this on a day-to-day basis have been elusive for many financial services organizations.
For example, you’ve probably already gone down the path of sustainably linked products and loans. Maybe you’ve used second parties who have signed off on the greenness of investments or the societal and sustainability assurances you are looking for. Capturing “geraniums” makes it cheaper for you to borrow money, after all.
When you get into more structured investment products, you’ll want to know not only about the entity that issued a deal but also the underlying assets and loans within the deal—and AI can make a big impact here. As bonds are issued, issuers can describe them however they want as it relates to ESG, but you will need the tools to analyze them to meet your standards.
One of the challenges we face in this industry is the lack of a formalized set of green, social, or sustainable standards. And in the absence of a regulator defining what those standards should be, we’ve been left to our own devices.
Now we see a trend for ESG reporting and analysis to come under the purview of the chief financial officer in many companies. While ESG metrics tend to be nonfinancial, they are becoming more material. Things like board diversity and FEMA risk declaration zones could ultimately have impacts on the profits and losses of your corporate clients.
Let’s look at one example where AI can help you take a next-level ESG approach: commercial mortgage-backed security. The cash flow is made up of mortgage payments on many properties and investors will want to understand the ESG profile on each of those properties. They may want to know about things like energy efficiency, green certifications, solar panels installed, or how close properties are to public transportation. This information won’t be listed in the deal documentation.
By using AI, you can use geolocation and spatial analysis from external sources to indicate whether properties are in flood zones or near wildfire danger zones. This is a real-time saver when you have many different properties to analyze.
There’s also the ability to analyze loan and securities documentation that is hundreds of pages long. AI can help the process, analyze, and ask questions in a systematic way. This can save hours of research time and deliver more accurate results than humans can manage. All this information can help you build predictive models to better understand the “greenness” of your investments.
By combining the KPMG Ignite AI platform with human subject matter knowledge, we have been able to create innovative solutions to many other client challenges with ESG. AI must be taught through examples to ask the right questions and deliver results. This technology creates the data foundation you need to manage ESG. And when you put it in the hands of people who understand regulations, securitization, and commercial banking, you’ll have an unbeatable combination.
KPMG works with many financial organizations to help them put the fundamentals in place, guide them through the process, and help them take green initiatives to a new level. Please reach out to me if I can help you get started.