Data-lite: How technology can fix green finance’s biggest challenge

This series of blogs looks at the opportunities and challenges associated with green finance in Southeast Asia, and draws on a recent position paper “Green Finance in Singapore and ASEAN: Opportunities and Challenges” by EuroCham Singapore and Accenture.

A lack of good-quality data on companies’ environmental profiles is hindering the uptake of green finance. Without such data, banks can’t know how exposed they are to environmental risks.

This links to a core difficulty of green finance, as outlined in the EuroCham Singapore report: “The question of whether the asset underlying an instrument is ‘green’ persists throughout the lifecycle of the instrument, from the issuance of debt to its maturity.” Regularly verifying an asset’s green credentials is expensive, which can deter issuers.

These are increasingly problematic issues. In response, the Monetary Authority of Singapore (MAS) recently released its guidelines on environmental risk management for banks in which it noted four areas of concern:

  • Climate change – the amount of greenhouse gases that businesses emit
  • Loss of biodiversity – the damage done to life by businesses
  • Pollution – the pollutants that businesses emit
  • Changes in land use – for those businesses that use land in their operations

Data problem = technology fix

Gathering the data around these elements of almost any company’s profile is extremely hard. The good news is that defining this as a data problem means technology can help resolve it. With the right tools, stakeholders can collect, structure, clean and analyse data, and then use other tools to visualise and report it.

Take pollution and changes in land use – two of MAS’s areas of concern. The owner of a coal-powered plant, for instance, could place IoT-enabled monitors in the chimneys that would track and provide pollution data in real-time. Or, to monitor land-use, drones could be deployed to send images that would automatically be analysed to determine whether, for example, illegal clearance operations were underway.

Collecting data has therefore become far easier and, as 5G is deployed, any bandwidth issues should be resolved. Instead of lacking sufficient data to make decisions, banks will face a different challenge: how to collect and analyse greater volumes of data and use them for green finance. Solutions here include data analytics, machine-learning and artificial intelligence (AI). This is leading to the rise of new fields such as Spatial Finance, which integrates geo-spatial data into the financial ecosystem.

Shades of green

More data means banks can better assess risk and more easily understand the ESG profile of their clients, and can therefore more easily extend the green finance universe. That’s important because sustainable finance today typically involves large, publicly traded firms in mature economies – as those are the very firms that already collect and share information about their green activities. It’s one reason ASEAN punches below its weight on green finance: its business universe is comprised of smaller, family-owned companies that lack the ability to measure or divulge such data, which means most firms in the region can’t access green finance.

However, as MAS’s guidance to banks makes clear, this is changing. The expectation from banks is increasing too: they are increasingly expected to play a societal “Good Samaritan” role in transitioning companies and economies to become more sustainable by channelling capital and resources to sustainable companies, activities and projects – pricing them better and engaging customers and broader societal stakeholders to adopt more sustainable practices.

This shift isn’t limited to Singapore. Thailand and Malaysia are also moving towards ensuring banks are more aware of their clients’ environmental risk. In the next three years, I expect all the major economies in the region to go down this route. By then, it will be increasingly common for banks to receive and analyse an entirely new universe of data, using cloud-based, AI-driven analytics to handle inputs as varied as numbers, text, language and images.

A more sustainable future

Some countries are already deploying technology to these ends. China – whose outstanding green lending is the world’s largest, and its green bond issuance the second-largest – has rapidly emerged as one of the leaders. There, fintechs are enabling green finance by developing innovative methods that cut the cost of green certification, improve green credit-ratings systems for SMEs, and boost banks’ capacity to identify and classify green projects.

The People’s Bank of China (PBOC), for example, has a Green Finance Information Management System that, among other things, connects it with financial institutions, collects green lending statistics, and ensures some regulation of green lending processes. By using big data, AI, cloud computing and other technologies, the system makes data traceable, comparable and measurable, which addresses common challenges like poor data quality and a lack of real-time reporting. And because financial institutions must report the details of each green lending transaction on a T+1 basis, the PBOC knows the state of green lending at all times.

To my mind, the direction of travel is clear – and pressure isn’t coming only from the regulatory side. Consider the recent letter to CEOs by Larry Fink, the chairman and CEO of BlackRock, the world’s largest asset manager. “Climate risk is investment risk,” he wrote, with the world “on the edge of a fundamental reshaping of finance”.

Simply put: Fink believes firms that are green will get more funding, and will grow; those that aren’t – or can’t demonstrate that they are – risk being priced out. As the report by EuroCham Singapore and Accenture makes clear, the green finance universe has come a long way in a short time. It is set to go much further yet.

Disclaimer: This content is provided for general information purposes and is not intended to be used in place of consultation with our professional advisors.
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