In a time where digital transformation dictates the financial industry, companies are finding it challenging to decide what technologies to adopt, and at what scale. The level of technology adoption depends on a few critical factors, one of which is resource commitment. Increasing digitalisation and technology adoption can be extremely demanding in terms of computational and human resources.
Energy consumption is a key metric to consider in implementing cutting-edge technology solutions, as rising energy consumption leads to higher overall costs and carbon emissions. Against this backdrop, integrating sustainable technologies is imperative.
In this article, we look at how Artemis AI, a signature code optimisation platform developed by TurinTech, can help companies in the financial industry implement technology solutions more sustainably.
Performance vs emissions: The dilemma in software development
The finance sector is known for its intensive use of data and computational resources. Financial institutions such as banks particularly work with large amounts of sensitive data. This necessitates such institutions to look for reliable and scalable software solutions to make the best data-driven decisions.
Activities such as detecting fraudulent transactions, credit approval and instant payment processing are critical, high-risk, and time-sensitive tasks, making it essential for financial organisations to invest in the best possible infrastructure to ensure the integrity of systems. However, from everyday banking transactions to high-frequency trading, the energy demands to implementing such technology solutions in the finance sector are immense. This ultimately pushes up company costs, increases carbon emissions, and significantly impacts the environment.
How big is this beast?
Looking at energy consumption and carbon emission figures is helpful to understand the impact of the financial industry on the environment.
Energy costs: A server is estimated to consume around 1,800 kWh per year1. If a financial institution utilises 1,000 servers for computational tasks, this will cost the company around £612,000 per year in energy costs2.
Carbon emissions: WWF UK and Greenpeace UK, in an analysis of the global emissions of the UK financial sector, estimate carbon emissions associated with selected UK private financial institutions to amount to 805 million tonnes CO2 equivalent, based on year-end disclosures from 2019.
This is almost 1.8 times the UK’s domestically produced emissions. If the financial institutions in this study were a country, they would have the 9th largest emissions in the world – larger than Germany’s (776 million tonnes CO2 equivalent) and Canada’s domestic emissions (763 million tonnes CO2 equivalent).
Figure 1: Emissions of UK financial institutions, recreated from: https://www.wwf.org.uk/sites/default/files/2021-05/uk_financed_emissions_v11.pdf
Artemis AI: A step towards sustainable finance
In an industry where software performance is critical, code optimisation is an efficient but often overlooked approach to reducing the energy consumption of software, without compromising on performance.
Artemis AI is a state-of-the-art automated code optimisation platform developed by TurinTech AI.
Artemis AI is capable of optimising code bases in a matter of minutes, resulting in more efficient software. As energy consumption directly correlates with software efficiency, implementing Artemis AI can lead to significant energy savings.
TurinTech AI research and calculations show that with Artemis AI, the energy consumption of servers can be reduced by as much as 46% per server, leading to cost savings of £281,520 per year for a business with 1,000 servers.
The Greenhouse Gas (GHG) Protocol defines three scopes for GHG accounting and reporting purposes. Scope 1 covers direct GHG emissions, scope 2 covers emissions from the generation of purchased electricity consumed by the company, and scope 3 covers other indirect emissions.
By optimising code, Artemis AI helps companies reduce the computational load on servers and data centers. This results in lower carbon emissions associated with data processing and storage, as well as running software, directly contributing to the reduction of scope 1 and scope 2 emissions of a business. Code optimisation may also lead to reductions in scope 3 emissions, for instance via reduction of emissions in products sold.
Artemis AI also helps companies significantly reduce energy consumption. Typical server wattage is estimated to be around 1,800 kWh/server/annum, whereas greenhouse emissions are estimated to be 0.3712 CO2-eq in kgs/kWh. By optimising code for better performance and lowering energy consumption by 46% on average, we estimate that Artemis AI can help companies reduce emissions from 668 CO2-eq in kgs/server/year to 360 CO2-eq in kgs/server/year3.
Given the importance of ESG in the current business landscape, Artemis AI demonstrates how technology can be harnessed to meet ESG goals. As the financial sector continues to evolve, tools like Artemis AI will be instrumental in ensuring that this evolution is not just technologically advanced but also environmentally responsible.
