A Q&A with Samuel Kuborn, Managing Director, Product and Profitability for Trustee, Depositary and Fund Corporate Services at RBC Investor & Treasury Services.
Has Covid-19 impacted your operations?
There was little impact operationally since most of our processes were already automated globally. For example, our technology and operating model provides us with the ability to produce the NAVs from multiple locations.
In March 2020, we switched to working from home overnight. Although this change was abrupt, our ability to make this transition seamlessly demonstrated the effectiveness of our business continuity plan. Going forward, we will leverage this experience and the firm will offer continued flexible working conditions. There are however remaining certain regulatory limitations for certain locations or the important to maintain employee engagement while working from home.
The survey shows that less than half (47%) use workflow and exception management while 41% cite a lack of automation as their biggest operational challenge. Do these figures surprise you?
For over ten years, we have had an automated NAV oversight process in place which allows us to monitor the accuracy of NAVs before they get published. The main benefit we get from NAV oversight is speed, accuracy and efficiency. We can release the NAVs quicker for the benefit of our clients.
Is there a place for machine learning (ML) and artificial intelligence (AI) in the NAV oversight process?
I definitely think there is. Workflow and exception management is a necessity, especially when you are operating at scale, and AI and ML will help with the remaining gaps that we have not been able to close.
For example, ML, which allows a machine to automatically learn from past data, can be applied to some of the more complex processes that are difficult to standardise or automate today.
AI, which is doing human intelligence tasks but faster, is likely to be used further in the future in our industry.
What is your biggest ESG data challenge?
ESG is complex and norms don’t exist. The industry is still organising itself and its main challenge is to manage ESG in a standardised, transparent and comprehensive way. Each of these three aspects is equally important and challenging.
Lastly, current ESG metrics give varying results and this lack of consistency between ESG data sources may give rise to cherry-picking. One rating agency may tell you a company is ESG-compliant while another could make it appear on your exclusion list.
What is the implication of a lack of standard ESG data?
If there is no trust in the underlying data, it will affect the credibility of ESG funds and if there is no pressure to publish reliable data, there might be a risk for greenwashing to occur.
We can, however, expect that in the next two to three years, the market will evolve and a global standard will emerge with an abundance of ESG data available in the public domain.
Will there be a cost to solving this ESG data issue?
What is costly today is the lack of a standard data model and getting access to reliable data. I believe that when reliable ESG data will be copiously available due to multiple data sources, then that should put some pressure on the costs.
What is the key to solving these data issues?
Although ESG is complex, the industry is restructuring and change will take time. The SFDR and the EU taxonomy, which comes into effect in January 2022, is a welcome mandatory step towards standardisation and transparency.
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