Johnny Mattimore, Global Head of Risk and Sustainable Finance at First Derivative, identifies three building blocks for data-led financial institutions in a new era.
Financial institutions are not equipped or structured to deal with the coming wave of sustainability regulation, period. 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. Financial institutions face an avalanche of carbon-based rules, regulations and standards. The changes are moving faster than many anticipated as businesses, governments, stakeholders, and individuals worldwide move toward more sustainable practices.
This leaves many decision-makers scratching their heads. 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?
Climate risk management – it’s a case of do or die
The catalyst for increased and accelerated carbon regulation will COP26, in November. One of COP26’s stated goals is to mobilise finance to help deliver global net zero-carbon emissions. Future financial regulation will put extra pressure on the financial sector by demanding rigorous climate risk reporting. This includes the Basel V accord, which I expect will be shaped fast during 2022 and 2023 with a long series of implementation milestones.
These new laws – and the speed in which they are arriving – means it will be a case of ‘adapt or die’ for many in the financial sector. Financial institutions can’t kick the carbon regulation can down the road. They must embrace climate risk management. In the same way that a business today has a ‘risk appetite’, it must modify this tomorrow to include a ‘climate risk appetite’.
To be frank, the financial services sector simply does not have the required talent yet to manage the data; as physical science is incorporated into the 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.
There are three key building blocks which are needed to create a data-led financial institution that is fit for purpose in the era of sustainable finance.
First comes data taxonomy, which enables financial institution to classify the data needed to create a structure for ingesting, normalising and taxonomising it. This means defining a narrow set of metrics and through various iterations improving its design.
Do not expect to get it right the first time – have a plan to build-test-improve-repeat, designing a structure by which you can easily expand the scope of metrics. Make sure the metadata, data catalogue and schemas are designed from the outset so that data discovery and changes in climate risk data standards (as the market taxonomy evolves) can all be provided to users, modellers, developers, and machines. This is the first building block of a robust approach to sustainable finance.
Accurate modelling is then required to translate out the incredibly complex raw data to simpler-to-understand risk-based output data metrics. This means a shift away from traditional data and modelling to use new ways of working – this transition will likely be in flux for some time to come. As such, it is advisable to not lock your firm into vendor relationships. The better option is to set out a universe of vendors and prioritise them while maintaining alternatives. Financial firms must create an R&D environment where different vendors can be substituted and the impact on metrics be compared.
This means adopting a components-based approach to any solution. This will allow the flexibility to switch and change as data and models evolve, which ultimately gives you a competitive edge. Some vendors will mature to be leaders, others to be laggards, and some will fail – it is vital that firms are able to adapt and move.
Fortunately, we live in an enlightened era for finance, where collaboration and value-chain partners are becoming the norm. Given that the data and models required are quite different to traditional finance, and that they will be in flux for some time, it may prove advantageous to buy rather than build.
The sheer volume of data required to report effectively and to remain compliant in a carbon regulated environment means that there is a significant element of machine learning and artificial intelligence (AI) built into our operating models to make sense of it all. It will then require human intelligence to oversee the processes and interpret the results with added skill and judgement.
Investment into analytics allows machines to analyse and then visualise information so that employees – with the AI support – can interpret meaning to inform critical decision-making.
Fundamental to this is machine-human monitoring, which allows for a basis for identifying strengths and weaknesses in the measurement process. Using it to drive iterative improvements in measurement by having formal reviews of data and model processes.
Constant learning is key to ensuring that data is managed effectively and impact is made. We see natural language processing being used to ingest human commentary to enhance data analysis and we see natural language generation being used for promoting and reporting issues. Together, these can form a virtuous cycle whereby machines and humans promote and demote issues as they evolve and trend. Not only can this make data analysis faster and higher quality, it provides a source of history to appraise the performance of humans and machines in across any risk management function.
How you can get ahead of climate risk
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. The challenge is that the financial sector does not have a stable framework to undertake what the regulators are asking them to do. It will be key to partner with the right consultants and data experts to shape this future modelling.