Why a digital transformation?
There is no doubt as to whether firms should invest in technology. It is a hygiene factor, a way of establishing a USP and if it’s done well, it’s transformative to the way any business operates. But underneath the sheen of agile working, of AI (artificial intelligence) experiments, of ping pong tables and beanbags. There is an unspoken truth wringing around the city, “All digital transformations are equal, but some digital transformations are more equal than others” (please forgive the paraphrasing). The pandemic has tested firms’ abilities to adjust their operating model and delivery; this has exposed the levels of difference in our industry.
Some seamlessly shifted towards remote working and digital delivery, while others were left wondering how they could buy 1000 laptops during a global laptop shortage. In fact, McKinsey has suggested that the existence of digital products and services within firms has been accelerated on average by seven years as a result of Covid-19.
So, who am I and why am I telling you this?
My name is Adam Jones. Over the past 10 years, I have held a variety of roles in FinTech which has seen me as a practitioner, a consultant and now a leader. I have two jobs, I am CTO of Redington and I am also Managing Director (MD) of ADA Fintech. ADA Fintech is a fintech founded by Redington which sells research management systems (RMS) to asset managers and asset owners. It’s informed by our view of what great research looks like and focuses on the end-to-end research process from data gathering, combining quant and qual information, embedding governance and audit and demonstrating value to stakeholders.
Accepting that technology has to be viewed as part of our proposition. How do we do that in a compelling way? And what’s the impact of not doing it? If our proposition is how we present and deliver services to clients, the question becomes, how do you digitise this in a way which improves our overall value?
If you’re running an investment research process you’re bound to have a series of different capabilities that you need to fulfil. Analytics, risk modelling and research management to name but a few. You’ll find a very active market of best of breed software vendors for these functions. That said, if you’re going to try to convince me that you’re running £5bn on a combination of Microsoft Word, Microsoft Excel, and hope, then we have very different worldviews as to what’s suitable and robust.
Ultimately, you have to ask yourself what is appropriate for your level of complexity, scale and risk. If you’re running things on systems which are fit for purpose you’ll have a knowledge advantage, a process advantage, a collaboration advantage and a transparency advantage. Without them, the process becomes a knowledge risk, an audit risk and a key person risk. Turning our attention to the output of our work (reporting and sharing data and information) people’s expectations are shifting daily.
Remember people aren’t judging the presentation of your tech against other asset management firms, they are judging it against Facebook, Apple, Google, Amazon and Netflix. If you want to add value and stand out then sending your clients a portal that allows them to self-serve, select their own view and drill down into the things they care about is imperative. In three years’ time, this will become a hygiene factor.
Broadly speaking we can break our world down into two buckets, quant and qual.
For quant, we look at an ever-increasing volume of data. In addition to the depth of historic data available to us relating to markets etc. We’re also seeing an increasing breadth of data that’s being used (e.g. social data and communications data) increasingly we’re seeing that feed through to how people are making decisions. Because of the increased data footprint, firms have to ask whether the data is solid, and of good quality.
For qualitative data, we are seeing a significant increase in the amount of data we gather over time. But while our data set if getting bigger, we need to make sure that the quality of the data is good. This brings with it challenges around integration and data quality assessment because of these different challenges, and this results in needing a mixture of people with different skill sets.
Qualitative data is on a journey of transformation. The industry has realised, through some difficult lessons, that we cannot simply rely on performance data, and that we need a bigger and more comprehensive picture of what is going on behind it. We now need to gather more data from more sources.
For example, 5 years ago we were asking 28 questions of managers for a particular selection exercise, now we are asking 48. Because of this change, your process cannot remain the same. You need to be able to systemise the collection and management of data. We have done this with ADA Fintech to empower users to gather and manage information.
Innovation in asset management has become a euphemism for AI and machine learning technologies. Everyone needs to be thinking about how they incorporate this into their tech roadmap and needs to find the right way of delivering for them as a firm.
In the same way that AI can find the optimum way of playing breakout, chess or go, firms are looking to run similar models on risk, reconciliation, trading and order management. The barrier to entry for AI can be high, and while some firms will want to create their own research function, there is a significant cost associated with this (and a big challenge around skills).
As a result, many may not want to or be able to take this route. In this case, firms can look to make use of AI services offered by big tech firms to find a way of embedding AI technologies into existing systems and processes at a low cost.
So how does the composition of these three threads, proposition, data and innovation contribute to a new world order for investment researchers?
If we go back to our qualitative assessment example, what we’re seeing here is an increasing recognition in the value or quality of data and the complexity it can cover especially if you’re a high conviction advice practice, you’re operating with retail clients or in the multi manager portfolio space.
Quant provides the screening and qual provides the selection and also the justification. So, quant takes us from 200 Euro Credit managers to 10. But qual gets us from 10 to the one manager we are going to recommend. Because of this involvement in the qual process, our ability to evidence decisions is also key. By using new technologies and a robust approach to data you can systemise your decision-making process and build an evidential data trail.
Effectively, you become your own qual data source. And in doing this, you build your own data set and also bank auditability for the regulator and provability for the fund selection. In summary, and perhaps unsurprisingly, digital transformation is really hard.
All firms are on a digital journey but at a variety of different paces. We’re seeing a massive proliferation of data across our industry as a result of this transformation. AI is becoming embedded in many processes, but typically still has human oversight in its application.
For more information on how ADA Fintech can help you please get in touch with our MD Adam Jones.