Achieving profitability at scale: the potential for AI

We delve into the use of AI technology in wealth management. Far from belonging to a far-distant future, it is here and growing more intelligent by the day.


As the wealth management sector seeks more efficient and scalable ways of working, many are turning to the potential for Artificial Intelligence (AI):

‘Artificial intelligence is a machine’s ability to perform some cognitive functions we usually associate with human minds.’

McKinsey & Company, What is AI?

So, are the machines really coming for our jobs, as recent headlines imply? The reality, for now, at least, is far more nuanced, with AI shaping up to be a new enabler for a more agile and efficient operating model. And despite initial skepticism and resistance to AI technologies, there are signs that the industry is gradually shifting its stance. The Schroders UK Financial Adviser Survey 2023 paints a particularly progressive picture with statistics revealing a significant 70% of advisers are open to ‘embracing its potential’, and an even higher ‘85% anticipate incorporating AI-based technology applications into their advice process in some capacity in the future’.

In an industry where technology is becoming a determinant of survival, AI is therefore poised to become integral to the delivery of a hyper-personalised client experience and the empowerment of internal teams. Recognising AI’s burgeoning capabilities and considering how they can be best utilised within their specific operating model should now be climbing to the top of industry leaders’ priority list.

AI in the wealth management context

AI technology includes a considerable range of capabilities that are well-suited to the complexities and highly regulated environment of wealth management. Beyond automating repetitive, time-consuming tasks, AI can analyse vast amounts of financial data to ensure investment advice is wholly appropriate and closely tailored to each client’s individual goals, risk tolerance, and personal situation or life stage.

Risk can also be reduced as advisers are kept one step ahead via AI algorithms, which can assess risks in real time and adjust investment strategies accordingly. Such timely interventions, in conjunction with human advice, will likely increase customer satisfaction and retention as clients’ losses are minimised and returns are maximised.

Predictive analytics, driven by machine-learning algorithms, are continually improving and enable firms to foresee market trends and anticipate potential risks, thereby creating a level of responsiveness and decision-making that will feel truly intuitive to clients. Add AI’s ability to analyse client behaviour and sentiment to gain a comprehensive picture of the client’s investment preferences and biases and you have the perfect tool.

The challenges

That is the theory, anyway. In fact, implementing AI into the wealth management infrastructure has proven problematic for many firms over the past five years. For while some have smoothly integrated AI into their operations, others have had difficulty transcending ‘proof of concept inertia’ in their endeavours.

Despite demonstrating AI’s potential value to the business, transitioning to full-scale implementation can present numerous issues, including data quality issues, a dearth of technical expertise, and an entrenched cultural resistance to change. Data quality, particularly crucial in machine learning, poses a major challenge because models rely on copious amounts of labeled data for effective training, which is particularly arduous to achieve manually.

A solution is at hand

Enter Large Language Models (LLMs), such as Chat GPT. These models have undergone extensive training on vast unlabelled datasets, enabling them to discern and extrapolate patterns from text without explicit labeling – something of a game-changer. Consequently, they are proving invaluable across diverse tasks and applications, especially where data is unstructured or scarce. Notably, LLMs exhibit agility in adapting to new data and can streamline training and implementation efforts.

It’s a development that means the potential applications of AI are primed for rapid growth. In the next 3-5 years, we predict LLMs could revolutionise key wealth management functions, particularly in client management, onboarding, compliance, and personalised communication. Leveraging natural language processing and machine learning, LLMs can automate time-sapping tasks like data entry, compliance verifications, and document processing, liberating wealth management professionals to focus on high-impact activities.

Just as crucially, the technology can enhance the client journey by delivering tailored and timely communication while pinpointing cross-selling and upselling opportunities. LLMs are also capable of fortifying firms’ regulatory compliance efforts. By automating rule-based triggers and interconnected workflows, compliance can be ‘baked into’ the client lifecycle at every stage.

Harnessing AI through specialist Wealth Management technology

At Wealth Dynamix, we are already harnessing the capability of AI and Machine Learning technologies. Providing our clients the benefit of cutting-edge technology within a solution purpose-built for wealth management teams, we give you the power of AI in a bespoke application.

Our solutions have been designed to revolutionise how advisers operate and engage with customers. Through personalised recommendations, they empower advisers to deliver tailored product and service suggestions based on an individual’s goals, preferences and needs while maintaining compliance every step of the way.

A full guide to our key AI and Machine Learning capabilities can be found here.

Spanning intelligent segmentation, sentiment analysis, speech-to-text technologies for transcribing calls and meetings, and AI-powered chatbots and assistants to extend normal servicing hours, AI is already helping our clients to be more efficient, compliant, profitable, and productive. It all begs the question of what tomorrow might bring.

Looking ahead

PwC research forecasts that assets ‘managed by robo-advisors will double in the next few years’. The 2023 Global Asset and Wealth Management Survey predicts the use of more AI-enabled digital platforms (to the tune of US$6 trillion by 2027), including an explosion of robo-advice, greater experimentation with the technology in the middle and back office and increased use for enhancing trading strategies and analysing unstructured data.

It is contributing to a fast-moving and dynamic environment, though as summed up concisely at a recent Banking Innovation Summit, the cleverness of this new technology also presents yet more risks for the banking sector, necessitating its potential in the wrong hands to be considered too.

“Artificial intelligence is one of the best tools we’ve seen come along to reshape our society. And it’s also one of the best tools ever for a fraudster… who’s trying to get into your bank, find information, use that identity, and get through the KYC and the onboarding the first time.”

 Cassi Chandler, CEO, Vigeo Allian, Former Bank of America & FBI Special Agent

Moving at speed

In summary, AI is something of a technology juggernaut, accelerating at speed but not yet at full throttle. With advancements in algorithms, available data, specialised hardware, and collaboration yielding more intelligent and versatile systems, its powers are likely to grow exponentially over the next few years.

In the sector of wealth management, where the high cost-to-serve can prove prohibitive to scaling the business or even holding ground to the competition, ignoring this potential superpower could prove foolish. Given the current rate of technological progress, the potential gains of integrating AI technologies are only set to increase, underlining the need for CIOs and CEOs to place innovation firmly at the centre of their business strategy.  

Beyond our bespoke CLM technology, Capgemini’s 2023 ‘Right the Technology, write the future’ features several current use cases for AI within the sector, from providing a personalised approach to retirement planning to using natural language processing to deal with incoming emails more efficiently. It’s all evidence that AI technology is already reshaping the industry. The onus now is on both firms and FinTech providers to keep up.

For a demo of our Cloud-based AI-enabled SaaS technology, please contact us.


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