AI is currently the most-discussed technology across industries. Popularity does not correspond with adoption rate. Wealth management firms have long known about AI’s potential, but most are unsure if it’s worth the investment.
However, money management needs to adapt. Strong competition, increased customer demand for digitised experiences and fee reductions, and an avalanche of new investment possibilities need companies to discover innovative ways to engage clients, generate leads, optimise work, and stand out in the market. As a result of the epidemic, many organisations are battling to gain new customers and retain old ones.
BFSI digitization has allowed wealth management organisations to build AI software. With more companies adopting AI, the time has come to move beyond technology discovery and pilot programmes.
Consider how this business has changed over the last decade to see why wealth management firms should reinvent themselves. Wealth management assets doubled between 2009 and 2020, from $45.6 trillion to $103.1 trillion. Low-cost products gaining market share, expanding middle-class income, and developing nations shifting from meeting needs to wants explain this. Despite the global epidemic, the wealth management business grew by 11%.
Maximizing these possibilities is difficult. 55% of wealth management organisations expect economic instability in 2020, says Accenture. Wealth management firms must be ready to respond to short-term market fluctuations and long-term possibilities.
The Most Significant AI Applications
Whether it’s market forecasting, banking personalization, manual task automation, or machine learning-based fraud detection, AI can solve the bulk of wealth managers’ problems with a carefully calibrated model architecture and appropriate data quality.
With 78% of enterprises currently employing client-facing and advisory-facing AI-driven technology, it’s a major test of their digital transformation capabilities, as well as a catch-up game for the other 20%. Let’s go over how artificial intelligence may help wealth managers improve workflow efficiency and generate more money.
- Generating Leads
Before analytics and AI, wealth managers had to manually collect and analyse data to find potential clients. Decisions were based on client demographics and net worth. Wealth managers can utilise AI to micro-segment prospects based on social media, news items, and public data sources, as well as find new leads and customise pitches.
An AI system can connect prospects with relationship managers that share similar hobbies, are the same age, or have worked with similar clients. Finantix, a California-based financial technology startup, developed AI-driven technology that can mine LinkedIn data to verify if the relationship manager is already connected to the potential client and generate the correct pitch message.
Chris Burke, Vice President at RBC Wealth Management, says AI-based technologies like NLP can process enormous volumes of structured and unstructured client data and optimise discussions based on prospects’ characteristics. Assessing how clients’ transactions respond to market events might help determine risk tolerance. Technology can increase wealth management organisations’ customer acquisition.
- Developing Customer Relationships
Wealth management and financial advisory success depends on actual client ties. We’ve reached a new era where clients expect a growing array of services, hyper-personalized financial guidance, and excellent user experience.
AI-powered employee-facing robo-advisory solutions help wealth managers meet consumer needs. Wealth management firms have a better chance of retaining clients if they give relevant and personalised communication.
Chief analytics officer Jeff McMillan said the system’s AI technology helps advisors build investment choices faster and more precisely. McMillan emphasises a system’s ability to discover clients’ interests and increase customer interaction.
AI-based fintech has also encouraged a trend toward cheaper financial advisory rates, with pricing models changed based on consumers’ investment profiles rather than service quality. Zero-commission pricing is Robinhood’s unique selling point. Flat-fee models require a deep grasp of customer characteristics and investment returns in wealth management.
A well-tuned predictive analytics solution can help discover high-attrition clients. Firms can then pinpoint clients’ pain points and prevent them from leaving.
Automation of Financial Advisory Services
In 2020, robo-advisor platforms and other methods to monitor the stock market with machine learning gained popularity, maybe because the epidemic reduced personal interaction and caused financial volatility. During the outbreak, Wealthfront’s account signups increased 68%.
Wealthfront’s robo-advisory technology provides digital-only financial planning and investment management. Wealthfront’s AI technology analyses a client’s saving and spending behaviour and recommends the optimum financial plan.
End-to-end decision-making automation has attracted interest, but it hasn’t won clients’ trust. Wealthfront modified their robo-advisory platform in 2021 to provide investors more flexibility and maintain long-term customer connections.
Vanguard, on the other hand, has also implemented an automated robo-advisor platform, but no action is made without the approval of managers and clients. Importantly, Vanguard has become one of the biggest players in the robo-advisor field with over $221 billion in assets under its supervision. This mainly supports our view that, in the context of wealth management, AI should not replace but rather aid people.
AI Implementation Difficulties and Solutions
Despite AI’s enormous promise in wealth management, only a few organisations have been able to deploy it at scale and make it a functioning element of their business. Let’s look at two major roadblocks to adoption.
Data Management
PwC research on AI for asset and wealth managers found that many organisations are cautious to scale AI because of its reliability. Data privacy is a big challenge in the wealth management industry, made worse by stricter restrictions. Uncalibrated AI will create more risks than opportunities.
The success of AI initiatives is closely tied to the maturity of the corporate data management infrastructure. An AI model’s output is only as good as the data it receives. Wealth management organisations should ensure their data is accurate, accessible, and compliant with regulations. Many wealth management firms have relied on manual data collection for decades, resulting in gaps in customer profile information and enormous amounts of unstructured data. This can be further compounded by segregated data repositories and the absence of a single data platform, for example a data lake or a data fabric solution.
Companies must rethink their data governance architecture to solve these concerns. Creating data standards, glossaries, quality assessment tools, and data governance duties are the first steps. Later on, it is necessary to develop data governance rules and controls, reporting frameworks, and automated data reconciliation tools.
Change Management and the Recruitment of Fresh Talent
After AI system reliability and data protection, fresh talent recruitment, employee retraining, and change management are the most difficult AI adoption operations.
Regardless of your company’s AI adoption, it’s important to inform staff of forthcoming changes. Assembling multidisciplinary AI teams, for example, explains a company’s strategic aim. Start with practical AI use cases to show the technology’s benefits.
Most wealth management organisations should start with back- and middle-office automation. Early adopter companies are setting the way for AI-driven back-office automation. Other organisations can benefit from their mistakes.
Never overlook reskilling and talent acquisition. Training programmes and missing jobs must be identified quickly. Given the shortage of AI expertise, asset managers must develop a long-term talent plan to benefit from AI. Hiring new personnel is difficult since candidates should have technological and finance expertise. Businesses should bridge IT and business development teams and look internally for talent. HR predictive analytics may be effective in this case because AI-powered solutions allow organisations to quickly evaluate their existing staff and find and pick subject-experienced candidates.
Conclusion
AI is an investment manager’s treasure. Wealth management firms shouldn’t rush into AI adoption because of early adopters. AI requires careful planning and business-wide coordination. Wealth management organisations must analyse their market position, long-term goals, and technological readiness to maximise this technology’s potential and design a clear implementation plan. Those that can integrate AI into their companies’ workflows may become market leaders.
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