Risk and profitability have been the two main parameters for making decisions in banking. Nowadays however, both concepts are used together with the term “sustainability.” In the 1980s, it became evident that economic development caused both social and environmental abuses. Since then, emphasis on the interrelationship between environmental and social issues and the financial sector has been increasing.
A growing awareness among consumers, stakeholders, employees, and competitors is prompting an increasing number of banks to become more proactive in terms of sustainable banking, particularly concerning product development and financing sustainability projects directly. So, how does data modelling come into play?
What is data modelling and where have I heard about it before?
Data modelling is the process of examining data sets to draw conclusions about the information it contains. There are three steps to data modelling: defining data, gathering data, and analyzing data.
Data modelling earned recognition in the area of (Credit) Risk Management, where risk is intangible and cannot be seen or heard, and there is a need to measure it. Measuring is the first step to managing, after all.
What is its relation to sustainable finance?
We see that sustainability is also intangible and cannot be seen or heard. However, as with risk we all know it is there, we know what results from inappropriate management, and that we will suffer from the consequences. There are both internal and external drivers for banks to integrate sustainability in their day-to-day business. And, to find quantitative indicators that allow banks to build strategies in sustainability, data modelling plays a key role. Data modelling is used to understand the current situation the bank faces and to provide the best actions to achieve a more sustainable banking.
As measuring and reporting on sustainability (internal and external) becomes a regular process among banks, there is no doubt that the core footing for quantifying it is data availability and data quality. Hence the same strong data governance that has become common practice in the credit risk domain, focusing on dimensions such as data lineage, model reviews and data quality monitoring, becomes increasingly the standard for sustainability reporting as well.
What benefit does data modelling in sustainable finance bring?
The benefit of proper data modeling within sustainable finance is a two-way street. First, having a clear and transparent methodology helps to bring portfolios to a higher sustainability maturity level. This allows banks to be more in control through a better understanding of changes that occur within the portfolio. For example, UBS developed the “Global Environmental Risk Policy” which allows their investment banking activities to integrate environmental measures. Secondly, the insights gained in sustainability can be used to attract and retain customers by new commercial offerings. For instance, the development of environmental investment funds (Eco Performance portfolio of UBS) and the financing of sustainable energy (Solaris project of Rabobank).
 Environmental and Social Risk Policy Framework; UBS; March 2017.
 Sustainable Banking at UBS; H. Hugenschmidt, Y.Kermode, I. Shumacher, J. Janssen; 2017.
 The Changing Environment of Banks; M. Jeucken, J.J. Bouma; 2017.
Internally developed sustainability modelling opens the door to offer specific advisory functions specialized in sustainable finance (often through partnerships) which address specific points on how the client can increase their sustainability levels and therefore also their sustainability rating. These functions not only strengthen the relationship between bank and client, but also result in a win-win as the business model of the client improves. For example, the investments result in lower energy costs, better residual values, or more reliable supplies.
How can Capgemini Invent help banks on their journey towards sustainability? We support banks on a global scale in their efforts to integrate sustainability measures in the credit risk assessment process – for example, including sustainability together with credit data to consider sustainability as a risk driver on business models for clients, or by ranking banks on their sustainability impact and reporting on the degree of sustainability of their loan portfolios.
Both approaches, sustainability as a risk driver and sustainability impact modelling, can ultimately develop credit and business models which not only look for the highest financial returns but also for the highest sustainability returns.
For the future of banking, data analysis and data modeling project an ever-growing environment where data is the main driver towards a more “green finance”. The impact of data modeling in sustainable banking will continue to expand enormously in the coming decades as sustainable data in all sectors becomes universality available. This way, the future of banking is using data modeling to profit the planet.
Check my previous blog on Sustainable finance: a glance at the life of sustainable modelling. To learn more or discuss sustainable finance, connect with me on social media.