Use Of AI In Sustainability

There is a great opportunity to build excellent AI tools to help Sustainability teams build high-quality datasets and machine-learning models. Artificial intelligence technology represents a huge opportunity for ESG insights: with the right training, AI systems can quickly process large amount of ESG data, and make recommendations with remarkable accuracy.?

AI is remarkably effective in doing ESG insights; using AI, you can automate 60% of ESG decisions. However, there is real challenge: these systems are not straightforward to build and ESG data in particular poses unique problems – training AI is time-consuming and laborious. In simple terms, to train an AI system requires to show it as much data as possible – text, images and scans if your aim is to train it to read these. ESG data has to be categorised and annotated in order to tell the system what it represents – an ESG disclosure report, perhaps, or a satellite image including a potential troublesome climate risk area that needs investigating – so that the AI can learn about what it is looking for.

The problem here, is that not many has developed tools to help annotate ESG data quickly and easily so that large amounts of data can be fed into the AI system quickly. Due to the complexity, size, variables and unique nature of sustainability data, managers have to resort to traditional and difficult-to-use manual tools to perform annotations.

In that regard, the opportunity is to develop a set of specialist annotation tools designed specifically for ESG decision making. Using these tools, it is possible to reduce the time takes to train an AI system by as much as 60%. That represents a significant breakthrough, opening up the possibility of accelerating the applications of AI in ESG. The financial services industry is very open to such use cases.

It improves on the existing technology in several important regards. First, AI tools are designed bespoke for the ESG sector, rather than relying on more generic techniques that do not always reflect the nuances and specialties of sustainability use cases. In addition, the API first tools can be accessed quickly through a cloud platform and can be used without any prior training. Also, the platform includes a number of automation facilities, which can manage and accelerate workflows.

It's a value proposition that is quickly gaining traction in the banking sector, with banks from the US, Europe and Asia looking for tools through a software-as-a-service model, with clients paying monthly subscriptions, based on their user numbers, for access to the platform.

With the rapid growth of AI in ESG data settings, researchers need excellent tools to build high-quality data models at scale, combining machine learning, data modelling, data engineering, and scalable computing to deliver consistent climate intelligence and decision support recommendations. This is an exciting opportunity to scale rapidly with the source of ESG analytics, but it’s also where technology will ultimately have the most impact – driving planet friendly decisions.

Many leaders of sustainability and other decision-makers are asking these questions.

?Where do they look to for advice on their climate risk exposure??

How can they access open data to build decarbonisation models for their business?

How do they make ESG decisions faster?

The sheer volume of data required to report effectively and to remain compliant in a carbon regulated environment means we will need a huge element of machine learning and artificial intelligence (AI) built into operating models to make sense of it all. It will then require smart humans to oversee the processes and interpret the results with added skill and judgement.

The financial sector simply does not have this talent yet; this is a physical science coming into a financial world. Financial institutions are going to have to change their thinking and make assessments based on the available data. Smaller firms are at particular risk due to a lack of resources. Financial services companies in their approach to climate risk have appointed people in nominal sustainability roles. The challenge is that ESG modelling is often done by climate scientists & sustainability experts who do not always understand finance. Likewise, finance does not always understand ESG data modelling specialists. So, the immediate business challenge is how to accelerate bridging this gap.

The climate risk challenge – the devil is in the data

There are three key building blocks needed that translates complex, fragmented climate data into accurate, user-friendly, decision-useful intelligence on-demand to create a data-led financial institution that is fit for purpose in the era of sustainable finance:

1. Data Taxonomy - Classify data needed to create a structure for ingesting, normalising and taxonomizing it.
2. Accurate Modelling - Translate out the incredibly complex raw data to simpler-to-understand risk-based output data metrics.
3. Analytics - Use machines to analyse, then visualise information. This way, humans – with the support of AI support – can interpret what it means and make critical decisions

Tech Innovation race to Net Zero

There is a boom in nominal climate tech platforms for ESG data, disclosures, reporting, rating, emission calculations and carbon offsetting. However a host of data science based IP driven “ESG analytics” startups are also emerging that offer predictive analytics platforms to enable organisations to make climate friendly business decisions. These startups generally combine machine learning with ESG data modelling techniques, leveraging a mix of publicly available, proprietary, and customer-specific KPI data to train their models.

A key challenge that venture-backed companies in this category face is building offerings that are truly productized and scalable. Every prospective customer will have a unique set of climate questions and objectives based on its particular situation, geographical footprint, physical assets, business priorities, and so forth. This inevitably pulls these startups toward providing bespoke, professional-services-heavy solutions for each customer.

Consultancies can be profitable businesses but rarely achieve outsize, venture-scale outcomes. Time will tell whether one or more startups in this space succeed in getting enough leverage out of software and machine learning to build a scalable, category-defining technology company in climate intelligence.

How you can get ahead of climate risk: Get ready for sustainable finance

Risk management has been a key part of financial services companies’ competitive edge for a long time. Similarly, climate risk management could become a defining element in the credibility of a financial services institution. Its reputation and financial value, which are already inextricably entwined, are at stake.

Those that move first in this nascent market have most to gain, but they will need to prepare their organisation for the era of sustainable finance. This requires significant change from mindset to skillset. It will be key to partner with the right consultants and data experts to shape their future modelling. The challenge is that the financial sector does not have a stable framework to undertake what the regulators are asking them to do.

Climate risk management

Financial institutions & Large enterprises face an avalanche of carbon-based rules, regulations, and standards in the coming years. These changes are moving faster than many anticipated as businesses, governments, stakeholders, and individuals worldwide move toward more sustainable practices.

National lawmakers worldwide are focussing more on climate risk disclosure in line with the Task Force on Climate-Related Financial Disclosures (TCFD) framework. Greenhouse gas (GHG) emissions are a particular area of focus. Also, lawmakers are looking at the capital adequacy of financial services companies to stress-test their exposure to global warming

Complex and fragmented climate data has been virtually impossible for everyone to use. Financial Institutions are not fully equipped or structured to deal with the coming wave of sustainability regulation. This includes the many de-carbonisation and green taxonomies heading their way in the coming years. Globally, companies – from the smallest firms to the titans of industry – are about to be revalued upwards and downwards according to their climate risk valuation. This could mean decades of severe disruption like we’ve never seen before. Those organisations that fail to adapt will most likely cease to remain viable within as short a time as five years.

Businesses need to adjust their business model to take a data-led approach to climate risk management, so what looks like a mountable challenge can become a potential competitive advantage. The time to act is now.

I am passionate about sustainability and believe we have our own role to play in creating a sustainable finance sector that works for the benefit of all.?

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Barun Chandran