Growing affluent and mass-affluent segments herald new demand for financial advisory services across the Asia-Pacific region. Worldwide, McKinsey anticipates a substantial increase in households with investable assets between US$100,000 and US$1 million, reaching a total of US$4.7 trillion by 2026.
This presents a unique opportunity for financial advisory firms (including banks and insurance companies) to expand offerings to new segments but also challenges in how to deliver the expected bespoke relationship-based services at scale.
At the heart of the challenge is how the industry can serve the increasing demand for personalised services across the wider demographic at scale without being hindered by legacy systems and manual processes.
Generative AI is a new enabler for this — it will reduce the time and effort to generate engaging, personalised recommendations, insights and content for clients.
Increasing demands on financial advisors
Today, financial advisors find themselves spending three-quarters of their time navigating between increasingly complex systems, leaving only a quarter to build meaningful customer relationships. They also see gaps in accessing training material and product information to be familiar with the latest policies, products, and trends. Clients also demand increased access to relevant data and analytics to make their own decisions.
Generative AI is already creating value for the financial advisory sector
At SoftServe, we see how Generative AI is increasingly used for customer engagement and can offer solutions to the unique challenges facing financial advisors and the broader industry. Generative AI-powered virtual assistants like SoftServe’s Meeter-Greeter are starting to streamline customer onboarding, facilitate advisor matching, and enhance the overall client experience.
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Virtual assistants also aid advisors with responses to client questions and can create customised marketing and financial literacy content. In organisations with higher maturity, these virtual assistants can also assist in fundamental analysis, data gathering, and risk identification for decision-making. Leading banks like J.P. Morgan, Morgan Stanley, and Citigroup are already doing this.
Banks in Southeast Asia are similarly starting to roll out Generative AI tools, starting with internal ‘co-pilots’ like virtual assistants to improve productivity by searching across multiple knowledge bases and generating engaging content for marketing and client outreach.
Over the course of 2024, we expect many of these internal pilots to mature — they will be connected to more data and introduced to more employees. This will gradually be followed by a shift towards client-facing virtual assistants as familiarity with and confidence in Generative AI solutions increases.
Embracing and increasing trust in Generative AI
Leveraging Generative AI should not just be the responsibility of AI or innovation teams. Organisations need cross-functional teams with adept change management skills, senior leadership alignment, and sponsorship. Leaders must understand that Generative AI cannot fully be introduced within a short timeframe but rather requires a long-term commitment involving multiple shifts.
In practice, this involves coordinating Generative AI adoption with overarching goals, gaining stakeholder buy-in, setting up appropriate governance structures to manage risks, involving legal and compliance teams, setting priorities, allocating resources wisely, and determining measurable outcomes.
It also requires a willingness to experiment and fail in the process of getting Large Language Models (LLMs) to meet the high bar of excellence expected of client advisory. Perhaps 20-30 per cent of projects will make it all the way from proof-of-concept, minimum viable product to gather customer feedback, and into production.
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One helpful framework that guides thinking on increasing trust in Generative AI is the proposed model AI governance framework for Generative AI published by the AI Verify Foundation and the Infocomm Media Development Authority (IMDA) of Singapore recently.
The nine-point framework contains key ideas on accountability, model visibility, testing & assurance that teams should consider early in the development process and use as guidance for adding guardrails or other risk-mitigating mechanisms into Generative AI solutions.
Riding the Generative AI wave
While there are still many perceived limitations — reliability, trustworthiness, and privacy, amongst others — of today’s Generative AI solutions, organisations wanting to harness the multiple benefits of Generative AI solutions should not ‘wait-and-see’ but instead be proactive in starting their journeys.
This will allow them to adapt to shifting market dynamics and innovate more effectively from a position of strength as the technology and available tooling mature rapidly.
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