Artificial intelligence (AI) has quickly built a variety of use cases for itself across several Financial Services sectors over the past few years. AI-based technologies are increasingly being utilized to expedite more critical corporate procedures as well as to supplement human expertise in mundane chores. Wealth management is one well-known industry sector that AI is dramatically changing. The necessity for the highest degree of accuracy, flawless analysis, and the enormous amount of data to be analyzed is the cause of this.
Companies need to come up with new strategies for interacting with customers, generating leads, streamlining work, and differentiating themselves in the market due to fierce competition, rising customer demand for digitized experiences and fee reductions, and the avalanche of new investment opportunities. The wealth management sector is beginning to take advantage of AI in ripe use cases, and as a result, opportunities to investigate fresh applications are becoming more accessible. Therefore, the need for AI in wealth management industry, however, has peaked.
AI in wealth management: various methods
There are numerous methods for to use AI in wealth management, with the niches or strategies varied depending on the clientele segments they service, the types of investments they recommend, their general investment philosophy, and their AI capabilities. Services could include digital-only advice, hybrid advisory, or just enhancing portfolio rebalancing skills using AI-derived insights. And that’s just in the field of digital advising.
Some front-runners that stand to immediately gain as artificial intelligence is prepared to improve the numerous touchpoints in the wealth landscape are emerging.
Portfolio management paves the way for AI in wealth management
Huge data sets can be instantly processed by AI in order to produce insightful, context-specific findings. Financial Institutions (FIs) can make use of this functionality to produce portfolio insights that are responsive to changing and larger settings. Based on the objectives of the customer, robo-advisors offered by FIs, such as Vanguard and Charles Schwab, can create, manage, and automatically rebalance a diversified portfolio.
Next Best Action (NBA) and Augmented Advisory
With the development of technology that make it possible to access a growing amount of client data, AI can assist FIs in utilizing this ever-growing pool to generate customized suggestions for each client. The NBA system, created by Morgan Stanley, uses machine learning to take into account clients’ life events and produce highly tailored investment ideas in almost real-time.
Tax planning
For high-net-worth persons, taxation is a wide and significant topic. Within this sector, AI finds numerous application cases. There is a lot of room for both generic and customized AI solutions, from automating tax filing by correctly identifying tax-sensitive transactions to proposing investments for tax savings. New AI tax solutions geared toward various needs are thus already making their way onto the market. AiTax uses AI to scan opportunities and eliminate the possibility of human error, guaranteeing that clients pay the least amount of tax that is legally permissible.
Client Onboarding
Due to the stricter regulatory due diligence needed when screening their clients, wealth management businesses must adhere to Know-Your-Customer (KYC) regulations that are different from those of the rest of the industry. These labor- and time-intensive operations can be automated with the help of artificial intelligence while taking context into account. The Finantix KYC Solution, which uses AI-powered multi-language and natural language processing to verify people, is being implemented by Deutsche Bank Wealth Management. It entails evaluating unfavorable news and background data about current and potential clients, building extensive profiles of them by compiling, distilling, and dividing them into categories according to relevance and risk level.
Cyber-security
Wealth management companies are responsible for safeguarding the confidentiality of their clients’ financial information because an ever-increasing amount of data is being stored on cloud servers. This presents a compelling argument for the need of powerful, modern, real-time monitoring tools in AI software to quickly identify problems. A critical application of Goldman Sachs‘ $72.5 million AI investment fund is the prevention of cyberattacks using AI-powered anomaly detection tools based on real-time data analysis.
Summary
The impact of AI is only now starting to be understood by the wealth management sector. It is still adjusting to implementing the simple solutions that support portfolio managers and streamline operations. However, the financial services industry is developing to embrace innovations like open banking, improved third-party market accessibility, rising interest in ESG investing, and other sweeping developments. Wealth management companies have the opportunity to identify and create new roles for themselves in customer relationship journeys as the landscape evolves. Building on the remarkable capability offered by AI solutions, particularly in more recent fields like algorithmic trading and real estate investing, will considerably aid this evolving definition of wealth management positions.
We have barely begun to scrape the surface of what might be a completely changed wealth management sector given the abundance of AI use cases still available to be piloted.
