Posted on 03 December 2020
Automation: Spurring a New Era in the Alternative Fund Industry
Key considerations for COOs and CFOs in establishing automation programs.
Despite the significant progress made by the financial sector in recent years, the alternative fund industry lags behind others when it comes to digitization. Although web-based portals, digital signatures and digital onboarding are all steps in the right direction, the industry as a whole has been slow to unlock efficiencies and increase service quality through technology.
While the pandemic has undoubtedly fast-tracked the adoption of automation in finance as a whole, it’s also forced organizations to think and act rapidly in order to chart the best route forward. Throughout the course of 2020, many of the firms who’d already put automation technology in place were better positioned and more resilient as workers adjusted to working from home while bots continued processing at their full capacity. Against this backdrop, one may begin to question what the next era of alternatives may look like as automation programs accelerate.
In an exclusive interview, Danilo McGarry, Head of Automation, and Maximilien Dambax, Group Product Head of Fund and Corporate Services at Alter Domus, draw on their experience to give their take on automation journeys in the alternative fund industry.
Q: Starting with the basics, what’s the difference between AI, RPA, and machine learning?
Danilo McGarry: For me, robotic process automation (RPA), cognitive or intelligent automation, machine learning, and artificial intelligence (AI) are all part of the automation world. RPA and pure automation is born out of process improvement needs, while AI is created through machine learning algorithms by structuring data in deep learning and neural networks—making the art of data science crucial.
However, if you step back and look at why a company may be building AI capabilities, it’s often for the purpose of automating or augmenting their decision-making processes. It can be argued that machine learning and AI technology stacks also fall under the automation umbrella, given they’re alternative means to automate what a company does. The combination of the two is something I like to call intelligent or cognitive automation.
Casting away the jargon and breaking it down into its simplest terms, RPA is like the hands of a human; the ability to click on the mouse and type on the keyboard. Optical character recognition (OCR) is the bot’s ability to read characters on the screen, while natural language processing (NLP) is the bot’s ability to interpret language, giving you essentially the eyes and ears. So when you combine RPA, OCR and NLP, you have the basic components of a human being. From there, machine learning allows humans to show the bot the ropes, allowing it to learn over time by connecting the dots using neural networks and deep learning.
Q: All things considered, where is focus best placed in an automation journey?
Maximilien Dambax: For a majority of firms, automation programs focus primarily on two goals: cost containment or reduction, and efficiency gains. While these are certainly worthy causes that resonate well at board level, they’re often missing the necessary transformation in the organization to become a true catalyst for end-to-end operational change.
The focus here should not only be on creating leaner processes, but also on new ways of operating, placing controls and focus at different levels with additional time spent on value-added tasks or risk areas. This approach helps get your current workforce onboard and actively participating in the program, which is critical as they drive the re-write of existing and to-be processes. The end result is a much longer-lasting solution that still captures efficiency and cost containment, but further highlights the time and financial investment necessary to get the organization to that level.
McGarry: I fully agree with Maximilien on this. Designing automation solutions and orchestrating them in a way that both humans and bots can work harmoniously alongside each other takes real outside-of-the-box thinking and execution. Scaling that infrastructure to create truly tailor-made AI capabilities takes not only time, but also the right people and the right company culture too.
It’s no wonder that the World Economic Forum has coined AI as the 4th Industrial Revolution—this technology is truly changing how companies, governments and societies think, work and behave. Such a transformational capability takes time to harness and must always be done with clients’ needs at the center of everything you do. At the end of the day, automation is all about improving client satisfaction and the employee experience.
Q: What type of methodology should be applied to automation programs?
Dambax: The automation revolution is here and accelerating rapidly due to Covid, so the big question now is not “if” or “when,” but rather “how is it best applied?” So long as there’s strong dialogue driven by the business’s need—what problem are we trying to solve?—then the right automation use cases naturally emerge. What’s paramount is getting the automation philosophy, design thinking, and agile mindset entrenched within operational teams.
McGarry: What Maximilien described is the exact methodology we used to introduce and continue fostering the automation mindset at Alter Domus. When running the automation operations room and building our automations themselves, we’ve followed a rigorous process analysis methodology to ensure we capture all the requirements carefully while also reengineering any inefficiencies that are found.
User Acceptance Testing (UAT) is a comprehensive process where we test every possible scenario and roll-out to full production, letting the bot do everything it was designed to do and continuously monitoring it for any improvements that could optimize performance.
We’ve taken a careful yet aggressive approach to automation, as we understand it’s a positive disruptive technology and want to use it carefully and for the right applications as Maximilien mentioned. In our portfolio of automations to-date, we have a delicate mix of low-hanging fruits, some medium-term projects, as well as long-term, highly disruptive uses of automation that have the potential to positively revolutionize the alternative funds space.
Q: What lessons have you learned in your own automation journey?
Dambax: So far in our journey, we’ve launched 13 bots, three R&D efforts, one global automation marketplace for people to order bots, as well as one process mining capability. Throughout this period, we’ve come away with a few key considerations to keep in mind:
Key Consideration #1: What business problem are you trying to solve
It’s important to ask “What are the true pain points of the service line?” In the middle and back office environment of the fund services business, problems are often triggered by the volume of transactions coupled with manual research, input, and resolution implied. Volume is one important dimension but risk and complexity are other areas where business teams should focus their attention with technologists.
Key Consideration #2: Where is data structured and controlled?
RPA is incredibly powerful in the areas of your business where you have a series of repeatable tasks, data collection or entry, and multiple manual steps with limited to no judgement required. In those scenarios, RPA is highly effective and accurate in the quest for automation, but falls short in providing real transformation if there is not enough time spent on the end-to-end redesign process.
Key Consideration #3: How can I plan ahead for adoption?
Efficient technology adoption generally occurs by means of a formalized process. It begins with the “why” but is also a matter of partnership throughout the organization between HR, client service, technology, and finance teams in order to achieve true process transformation. Prior to launching, you’ll need to know the metrics that will be measured to track not only the automation ramp-up but also the team’s automation usage over time. With the combination of the two, you’ll be better poised to maximize the return on investment.
Key Consideration #4: Which automation experts will help you scale?
RPA tries to replicate human tasks that have already been optimized. AI tries to predict things before they happen or augment a decision a human will make by providing them with better data. Whatever the application, it comes down to the question of “what does a human really need in order to perform better in their role?” Such requirements can only come from high performing subject matter experts that know the job, departments and industries well enough to think laterally. You’ll also need automation experts that are well-versed in the different uses of automation across industries so they can bring the benefit of outside industry experience as well. The true power of automation can only be harnessed through collaboration between determined and well-qualified people with the right business mindset.