AI and the Future of Wealth Management
Historically, high-net-worth clients are typically offered personalized advisory and financial planning services, whereas mass affluent clients that sit below a defined threshold are typically sold lower touch investment products such as mutual funds, bonds, insurance etc. However, with global incomes growing and a general rise in retail banking consumer appetite for personalized financial guidance, there now exists a large, underserved population of mass affluent customers eager for personalized financial guidance delivered by professional advisors.
However, capitalizing on this untapped demand often requires qualified (and in many countries, licensed) advisors, and as such has long been difficult to scale to a wider mass affluent audience, as the traditional delivery model is dependent entirely on the expertise of specialist resources. Now, however, technological advances are changing the game…
How Technology is Helping to Break Old Barriers
Newer technologies like AI, natural language processing (NLP) and large language models (LLMs) are enabling organizations to provide a high level of automation for complex processes that leverage unstructured data and completely transform customer journeys to achieve both scalability and an engaging user experience. From opening an account, to analyzing a client’s financial situation, to designing a financial plan and investment portfolio, even answering client queries, technology has the potential to streamline each step of the customer journey.
While these technologies aren’t yet at a point where they can fully replace all the functions of a licensed relationship manager (RM), those RMs who embrace and leverage AI and LLMs will have a clear competitive advantage over their peers who do not. For starters, these AI technologies will greatly enhance their productivity, allowing RMs to deliver personalized service to a greater population of clients. In addition, they also have the potential to significantly enhance the value provided to customers through sophisticated self-service channels, which, in turn, should help RMs to grow their population of clients willing to embrace self-service options.
As previously noted by EPAM experts, being able to effectively capture all structured and unstructured data, incorporate social elements, and feed actionable information and recommendations to relationship managers (RMs) are critical to this technology-driven transformation.
When this data foundation is in place, advisors’ time can be refocused to provide a better client experience by further refining and personalizing any guidance recommended by the AI platform and delivering it to the client with a human touch. This has three major implications:
- An increase in productivity for human advisors
- The ability of advisors to drive their contribution margins
- The establishment of a more ‘personal’ relationship between the client and the institution, improving client satisfaction and attrition rates
Taken in tandem, this will allow for personalized advisory services that are highly scalable, helping advisors to onboard and service a broader set of mass affluent customers.
AI is Changing the Game
Advances in AI (including conversational AI with LLMs or broader NLP capabilities) allow advisors to more easily automate tasks and enable democratization of personalized financial advisory services, including:
- Providing Insight into Unique Client Needs. Any effort to transform user experience and personalize an offering must start from a true understanding of the client. Leveraging data-driven research, client analytics and machine learning will play a critical role.
- Accelerating and Streamlining Onboarding. By generating personalized checklists for onboarding, AI can reduce the back-and-forth between RMs, compliance and clients, as well as support faster decision-making, and even provide additional channels for clients that prefer self-service options.
- Driving Engagement and Improving Financial Literacy. One of the biggest hurdles preventing mass affluent clients from purchasing certain investment products — other than simple products like retirement plans — is the fear borne out of unfamiliarity with these products and related terminology. Many well-intentioned tutorials try to explain investment products in dry, technical language, which only reinforces this negative connotation. Conversational AI can address this by allowing for a conversation in natural language that is engaging and even funny and friendly. This will be a critical design component to drive adoption of more digital personal advisory services.
- Providing Access to Digital Advisors. Market leaders are already moving to digital-first advisory models supported by RMs to service more mass affluent clients. ML algorithms can effectively generate proposals, balance sheet and cashflow statements, financial plans, asset allocations, and investment portfolios for clients. This enables advisors to spend more time directly interacting with clients to address questions and drive decisions, allowing RMs to make modifications and then automatically execute on those recommendations.
- Serving as a Digital Assistant to RMs. RMs spend enormous amounts of time gathering information and documents, scanning articles and opinions published by investment research teams, preparing proposals, presentations and scenario analyses for client meetings. With human error, they may miss something. AI and ML can be used to organize all required documents, recommend the most relevant research, prepare proposals, analyses and even presentations, which delivers large productivity benefits to RMs. Further, AI/ML can also be deployed to recommend “Next Best Conversations”, along with all relevant information to make client meetings more effective for RMs and clients alike.
- Providing Robust Compliance. RMs must constantly undergo complex compliance training, especially those servicing offshore clients in different countries. AI and geolocation technologies can act as an additional layer of compliance control, continually reminding RMs of nuanced restrictions, and only allowing those activities which are permitted by regulations. Supervisory checks and other compliance processes and risk management can also be automated in a similar fashion. This is not to say AI eliminate the necessity of robust compliance checks, but rather can complement those processes to add another layer of oversite.
- Providing Enhanced Automation of Back-office Operations. Prior to the advances in LLMs, it was challenging to automate the highly complex processes involving entirely unstructured data. While RPA, OCM and BPM could be used, these technologies were often limited to automating structured and semi-structured data-driven processes. With LLMs, it is now possible to automate across the spectrum of data and process complexity. This brings down costs sufficiently to provide wealth management firms with the option to profitably offer personalized advisory services, at scale.
It's worth noting that while AI has the potential to deliver a world of benefits to wealth managers, careful and explicit care needs to be taken to closely monitor and eliminate any potential bias within data sets and outputs to ensure these benefits are delivered in an ethical and compliant manner.
A Huge Transformative Leap
FinTechs, low or zero-commission brokers and robo-advisors are all chipping away at banks’ share of wealth management services. At the same time, millions of customers desire comprehensive, personalized financial advice and solutions. As global incomes continue to rise, this will present a huge untapped opportunity for financial institutions.
Though estimates vary, the mass affluent market share accounts for trillions of dollars of investable assets – mass affluent customers in the US alone are predicted to account for $47 trillion in wealth by 2025 – much of which can be channeled to professional wealth management with the right personalized financial advice. And while there’s no denying that RMs’ personal attention still holds value, technology and AI adoption will be vital to efforts to scale the process of personalizing financial advice to this population of mass affluent customers.
Banks looking to capitalize on this opportunity to build a competitive advantage need a clearly defined technology roadmap and the requisite systems to support the data analysis efforts that will fuel this acquisition.