AI and Robotics in Wealth Management: Step into the Future! – Part 1
The applications of Artificial Intelligence (AI) and robotics are seeing substantial growth in financial services. For those committed to staying ahead in the game it is helpful to get a clear view of the meaning, variations and touchpoints between the technologies, trends and real possibilities. Part 1 gives a general overview of the market.
Originally evolved as two distinct disciplines and branches of technology, AI and robotics are now coming closer, often overlapping and easily mistaken for one or the other. To understand the current convergence, we need to look at the origins and «pure» definitions:
Artificial Intelligence is a man-made entity (device or system) capable of performing goal-oriented tasks in ways similar to humans: perceive the environment, interpret it and make decisions leading to higher chances of attaining the goals. It emerged as a scientific discipline in the 1950s and is mostly concerned with modelling the human thought process in problem solving and decision-making. Since then it has evolved in waves of great enthusiasm (and investment) divided by a period of disappointment and reduced activity (known as «AI winter»). We are currently in a wave of great empowerment by computing progress, driving business interest in both adoption of available solutions, and investment in new developments.
The robotics industry, on the other hand, has a longer history – starting with pragmatic industrial applications: Programmable production machines were widespread decades before AI was born, while in the second half of the 20th century some of those machines literally mimicked physical human actions, e.g., the welding robots in the automotive industry. In the 21st century the robots entered the service industries, while simultaneously moving from the physical domain to the virtual/digital, where most of the processes occur in sectors like telecoms, public services and, of course – banking and insurance. These robots no longer resemble human arms, exist in the IT systems as software and automate the processes that still require human interaction between and around those systems. Thus the branch discipline of robotic process automation (RPA) was born, by now a mature industry in its own right, with multiple powerful vendors and endless success stories around the globe.
While AI brings increasingly accurate object and pattern recognition, «clever» reasoning and decision making that on occasion defeats the human one (as in certain games, famously won by computers against human champions) – and RPA quietly but relentlessly brings efficiency and risk reduction to simple repetitive tasks, they also extend towards each other’s territory. AI-based interfaces not only process natural language, they acquire some physical attributes like a human voice (Siri, Alexa) and even an animated face. Back-office robots, on their part, are using cognitive methods, becoming more intelligent and capable of conditional decisioning, not just programmed repetition.
Robot types and wealth management use cases
Robots can vary significantly in their mode of operation, place in the ecosystem/architecture, level of intelligence and main purpose for deployment. Each type is designed to bring a mix of tangible and intangible benefits like efficiency and cost reduction, improved quality, precision and user experience (CX), error and risk reduction etc. Some robot types, however, focus on one benefit area and enabling functionality, at the expense of another. There are no «pure» types, but based on prevailing attributes, the following have emerged:
Front-end (interaction) robotic applications
While providing some efficiency gains (e.g., in mass use cases like retail banking and consumer insurance), their focus is more on improved quality and enhanced user experience. This is where the robotic (humanoid automation) aspect overlaps with and utilises AI with learning and cognitive functionality.
One group under this branch is typically known as chatbots, but they have evolved well beyond simple chat (short-text, instand message-style interactions). Some of them are capable of interpreting longer texts (machine-read letters, emails) and composing meaningful responses. Others interact through speech – like the popular Alexa, Siri or Google Assistant. The life-like avatar «Cora», piloted by NatWest Bank, reminiscent of scifi characters, is said to even use AI to understand customer emotions and adapt the interaction accord- ingly. The same FinTech vendor, Soul Machines, of New Zealand is behind the latest humanoid bot «Sarah», developed for Daimler Financial Services.
Another human-facing class in financial services recently became notoriously popular as robo-advisors. Initially promoted as disruptive challengers to classical wealth management institutions, nowadays there is hardly an incumbent bank or wealth management firm without its own (home-built or acquired) robo-advisory proposition. These solutions have their own user interface of varying quality and user experience, but the focus is less on interaction management and more on automating the support for client investment decisions. Most of the currently running robo-advisors lack the sophistication and scope to cater for HNWI and UHNWI (high- or ultra-high-net-worth individuals) clients but address the needs of the «mass wealth» (the long tail of upper income/mass-affluent consumers, whose sheer numbers underpin a vast bulk of assets in need of professional management). The thinly spread cost of automated advice takes it even further into lower segments, offering affordable «wealth management» for the less fortunate masses and performing an added social function.
One must understand that the above front-end robotic types are not exclusively meant to deal with customers. They are equally efficient and user experience-enhancing in internal deployment for employees, as well as other stakeholders like financial intermediaries or government authority agents.
Back-office (operations) robotics
The emphasis here is on industrial-style automation, performing at speed standardised repetitive actions with predictable (and reliably persistent) outcomes. Established for at least a decade, robotic process automation (RPA) finds fertile ground for efficiency gains at the interface touchpoints. The prime candidates for such automation are interactions between a machine and a human (e.g., data entry or validation), or between machines with a human intermediary (i.e., in complex architectures with multiple systems).
Originating from an older technique known as screen scraping (capturing in electronic form data that has been formatted for visual display to a human user), RPA virtual workers now- adays routinely log into upstream systems with human credentials, capture output data and seamlessly enter it into the downstream target system. Most current RPA platforms and technology solutions are designed for rapid development of bots with minimum programming skills (or none at all). They have a visual workflow interface that enables drag- and-drop (re)configuration of process steps and tasks. The deployment of RPA takes weeks, rather than months; with immediate benefits driving strong ROI.
Some instances of RPA deployment in private banking and wealth management include (but are not limited to):
This often involves a lot of manual data entry, time- consuming prospect creation and account opening (sub)processes, several know your customer steps and, according to best-practice research, can potentially be up to 85% automated.
Reconciliation of securities
Laborious repetitive activity with significant manual component and use of office productivity tools (e.g., filling out Excel sheets) side-by-side with large and complex technology platforms can be handed over to RPA solutions. Completed pilots show up to 50% effort reduction, as well as much lower error rates.
- Rate and price uploadsThey represent another highly manual operation involving Excel sheets; whether in-house or business process outsourcing-performed, it can be streamlined with RPA for considerable savings and error reduction, leaving humans to only handle exceptions.
In all technology implementations, changes and updates, release deployments or maintenance interventions, testing involves a lot of human effort and consumes a considerable portion of total project time. Prime targets are highly repetitive test procedures, which can be automated with RPA and resources (re)allocated to training and other human-dependent activities.
Organisations where large numbers of invoices are regularly generated with input from various systems, involving manual work and critical validations, can benefit form RPA to automate at least half of these activities, reduce errors and disputed invoice resolutions, and increase timely payments to over 90%.
Filing of statutory forms and documents
One example are tax returns, where wealth managers are providing tax services to clients. This is a repetitive routine task, highly seasonal with excessive workloads near deadlines. Stress of overloads and deadlines increases the probability of human errors. This is very easy to robotise with compelling savings, improved accuracy and compliance.
Increasing sophistication through combination
While the above (and many similar) processes are simple in nature and early targets to prove the concept, larger and more complex processes demand increased sophistication and some RPA solutions are increasingly involving AI to replicate more complex human actions. In one example, «AA» (Automation Anywhere, one of the RPA industry global leaders) is offering «IQ Bot», a process automation solution with cognitive capabilities allowing work with unstructured data and/or natural language, decisioning (rules engine) based on discovery and interpretation capability, and integration with advanced AI platforms like IBM Watson.
Another example of convergence with AI and increasing sophistication is the above mentioned onboarding process, originally automated just for efficiency. The know you customer (KYC) and anti-money laundering (AML) components therein are very demanding on human input or higher machine intelligence. This is where the initial form-filling simple RPA goes through external system access (e.g., the «Thomson Reuters World-Check») to obtain information and bring it into the account opening process. From this level, the evolution is into fully-fledged AI solutions that crawl massive sources of unstructured data (dedicated databases, but also mass- and social media in any location and language). The KYC and AML solutions of vendors like «IMTF» or «SmartKYC» and parent «Finantix» are successfully doing precisely this, offering their user institutions unparalleled insight into client backgrounds beyond compliance requirements. At the opposite end of niche functionality and short implementation times, Synpulse is currently piloting an «adverse media screening» (AMS) solution based on technology from AI vendor Squirro that brings insight not just for onboarding, but for ongoing monitoring of existing clients, as well as for regulatory requirements.
AI and robotics have a lot to offer for the wealth management industry. They allow early adopters to leverage efficiency and compete in a climate of declining fees and margins. Whereas Part 1 gave a general overview of the market, in part 2 we will take a closer look at the concrete adaptions on the client side, describe benefits of AI and RPA in end-to-end operations and give concrete guidelines for wealth managers based on our UK Wealth and Asset Managers survey.