Natalie Monko Lead Project Manager

    AI-Based Wealth Management: Use Cases and Opportunities

    Traditionally, asset and wealth management (AWM) companies have to manually perform various tasks, from request processing to investment portfolio management. As digital transformation is gathering pace, client needs and preferences are rapidly evolving. Now, people are demanding faster response times and more personalized service. 

    However, not all organizations are able to meet changing consumer preferences. According to recent research, 65% of Americans claimed that businesses need to enhance customer experience, especially in the wealth management sector. Experts found that 15% of respondents stated their brokerage firm delivers the best user experience, while only 12% said the same about their providers of retirement services.

    Bringing automation in wealth management, artificial intelligence (AI) allows market players to boost client engagement, increase efficiency, and reduce costs. At the moment, AI software solutions are being widely introduced in asset management, too. 

    Grand View Research informs that the worldwide AI in the asset management market is anticipated to progress at a compound annual growth rate (CAGR) of 37.1% during 2020–2027. Experts at PwC revealed that by integrating AI, 78% of survey participants streamlined decision-making, 73% improved user experience, 68% raised productivity, and 49% cut expenditures.

    In this article, software experts at Surf will talk about the main use cases of AI in wealth management to help you learn how you can address existing challenges and gain a competitive advantage. Let’s get started.

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    Top 6 use cases of AI in wealth management

    1. Investment portfolio management

    Employing AI applications in wealth management, organizations can speed up investment portfolio management, this way increasing customer loyalty and relieving professionals from performing routine, data-intensive tasks. To accomplish these objectives, an AWM firm can utilize a robo-advisor. 

    Powered by AI, robo-advisors are not physical robots, but software solutions that collect information from users about their financial situation, investment objectives, and risk attitude. Then, a digital advice platform offers tailored recommendations that may show, for instance, how to balance an investment portfolio. 

    What’s more, advanced robo-advisors can automatically invest assets on behalf of their clients. Acting without or with little human intervention, these software programs allow AWM professionals to devote their time to other important tasks. 

    As AI-based investment advisors offer multiple advantages, the demand for them is quickly growing. The number of people using robo-advisors is predicted to reach virtually 478.9 million by 2025, as reported by Statista. CNBC forecasts that assets under robo-advisor management will climb from $460 billion in April 2021 to $1.2 trillion by 2024.

    A survey of US adults conducted by Vanguard found that on average 54% of respondents consider a robo-advisor a convenient way to invest. In addition, 35% of users stated that having an automated investment advisor would be great in a volatile market.

    When it comes to real-life project examples, let’s take a look at Betterment, a US digital investment company. Established in 2008, Betterment offers robo-advisory, retirement planning, and cash management services. Betterment provides a robo-advisor that enables people to invest small amounts of money and achieve optimal returns while requiring no minimum deposit to get started.


    As of today, Betterment has more than 700,000 users with assets under management of more than $32 billion. To date, the startup has raised a capital of $435 million.

    If you are wondering how to build a robo-advisor, read our ultimate guide on the topic.

    2. Customer request processing

    Overloaded with the growing number of tasks, employees may not be able to review requests in speedy fashion. As a result, people may have to wait for up to an hour until an expert provides the necessary assistance. This way, organizations encounter issues such as reduced customer retention and engagement rates, leading to lower revenues. 

    Customer request management is among the primary use cases of AI in wealth management. With the aim to streamline inquiry processing, AWM firms can utilize artificial intelligence-based chatbots that not only instantly provide answers to user questions, but also ensure round-the-clock support. 

    Unlike human professionals, AI chatbots do not get tired and process a variety of requests simultaneously. According to Invesp, conversational technology can help companies cut customer support expenses by 30% and improve staff productivity by answering up to 80% of routine questions. Business Insider reports that around 40% of Internet users globally prefer interacting with AI bots than virtual agents.


    Speaking of a technical side, AI-enabled chatbots can be integrated into websites, asset management platforms, messaging apps, emails, and other channels, which are already used to communicate with users.

    3. Compliance management

    In general, AWM firms have to rely on mundane, paper-based workflows for ensuring compliance with various standards like the General Data Protection Regulation (GDPR). On top of that, institutions need to consider new rules and demands for investment management agreements (IMAs), separately managed accounts (SMAs), wrap accounts, and other aspects.

    Financial experts have to manually study data updates, official announcements, and guidelines imposed by central authorities. As specialists may spend a few hours per day on these tasks, it may be quite challenging to timely take the necessary actions and achieve conformance with the regulations.

    AI algorithms, in turn, can monitor requirements changes introduced in orders, documents, investment policy statements, and other sources in real time. Then, an AI software system can generate reports and notify professionals about regulatory changes. Therefore, the use of AI in wealth management allows you to significantly facilitate compliance while improving employee productivity and reducing expenses. 

    Regarding real-world projects, the EY organization developed a range of investment compliance accelerators. For instance, the institution built SARGE, which is a cloud-based AI system for AWM firms. 

    The solution utilizes natural language processing to extract investment guidelines from governing contracts and correlate them with existing compliance rules. SARGE is also used for analyzing and categorizing valuable information. By employing AI tools such as SARGE, companies can cut compliance costs by up to nearly 75%.

    4. Personalization 

    Applying artificial intelligence in wealth management, organizations can hyper-personalize customer experience. By employing AI and machine learning, companies can analyze massive arrays of data such as in-app user behavior, past activities, feedback, and transaction history. 

    An AI system can segment the audience and create a 360-degree customer view. Thanks to this, AWM firms can deliver individual recommendations related to investment, financial wellbeing, and asset management. Since AI provides a clear understanding of client needs, institutions can optimize marketing campaigns, increase lifetime value, and prevent churn.

    However, tailored advice is only a part of personalized service. Consumers have different preferences regarding the way they want to interact with AWM professionals. AI software solutions are used to identify the most effective forms of communication (email, chats, phone calls, mail, messages) for every customer, as well as favored frequency of interactions. 


    Additionally, artificial intelligence can offer tailored advice connected with major life events like retirement or birth of a child. Using AI in wealth management, companies can also protect their clients against making wrong investment decisions by investigating their goals and evaluating market volatility.

    Here at Surf, our team has recently developed a mobile investment app that offers tailored recommendations according to client needs. Check out the case study to find more information about this software solution.

    An example of investment app functionality created by Surf (Source:

    5. Tax planning

    Tax planning is among the main applications of AI in wealth management. As thoughtful tax planning enables customers to save costs, it should be a substantial part of an individual investor’s strategy. However, people generally consider tax planning as a complex process that requires taking into account numerous aspects, for instance, the timing of income and purchases.

    In addition, accountants need to study various tax planning alternatives, prepare tax reports, and consult with financial experts. As a consequence, they spend a lot of time and resources while having a risk not to find the best option. 

    With AI technology, it is possible to speed up tax planning, this way boosting user engagement and increasing the retention rate. As for real-life examples, let’s have a look at Tax Planner Pro, a tax management assistant. 


    Powered by artificial intelligence, Tax Planner Pro compares tax strategies to identify the optimal variant for each customer. AWM firms also use this AI software system to view how different tax plans impact their returns.

    6. Sentiment analysis

    AI and wealth management can be paired to provide sentiment analysis and gain valuable investment insights. Financial institutions can utilize machine learning to evaluate how customers feel about assets and stock market conditions.

    Thanks to AI, it is possible to forecast how people will invest their funds and what strategies they will choose at a given moment. By predicting the movement of tradable assets, companies can deliver meaningful recommendations to their clients.

    As an example, MarketPsych Analytics is a software solution that employs machine learning and natural language processing (NLP) to enable sentiment analysis. The platform performs text analysis of 2,000 top worldwide financial news outlets, 800 social media websites, and tens of millions of authors to assess market perception around companies, countries, stocks, bonds, commodities, currencies, and crypto assets. 

    AI wealth management system recognizes and measures various sentiment indicators that relate to joy, fear, anger, optimism, confusion in terms of interest rates, price fluctuations, corporate mergers, and other factors. Finally, with the help of sentiment analysis AWM firms can create investment strategies, forecast volatility, evaluate risks, and offer actionable advice.

    Wrapping it up

    There are a lot of benefits of AI for wealth management. Employing AI software, companies can ensure customer support 24/7, process numerous inquiries simultaneously, offer tailored recommendations, and automatically manage assets on behalf of clients. 

    Thanks to this, organizations become able to improve user satisfaction, reduce expenditures, and maximize efficiency. Relieved from performing manual, routine, and data-intensive tasks, AWM professionals devote their time to other important and more complex activities, such as strategy planning. 

    If you want to create your own wealth management app, but are not completely sure what to focus on, feel free to read our article about wealth management app ideas. And in case you are going to build an AI wealth management solution or integrate AI into the existing software, you are welcome to contact our team. We will soon get back to you and help address all issues. With extensive experience in AI and wealthtech, we are ready to help you prepare a technical specification and outline a project implementation roadmap.

    Are you going to build an AI wealth management solution or integrate AI into the existing software?

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