Use of AI to Scale Wealth Management Business

The wealth management industry, considered one of the more traditional fields of financial services, is now no stranger to artificial intelligence and machine learning. These days, most businesses use it in some way. But its deployment has been mostly limited to the automation wealth management of repetitive processes. Plus, cost savings is the primary goal in this case, rather than delivering truly measurable value to the end-clients can feel. And remembering how AI has revolutionized practically all aspects of our daily lives (from music streaming to ride-sharing, budgeting, and alike), it becomes clear how most wealth-managing business s miss out on the full power and transformative value.

Content:

• How to Use AI to Scale Wealth Management Company?
• Why Use AI in Wealth Management?
• Use of AI in Wealth Management
• Benefits of Using AI in wealth management,
• Successful AI Use Cases (Examples)
• Challenges and Solutions for AI Implementation in Wealth Management
• Conclusions: The future of AI in Wealth Management

How AI Can Scale Your Wealth Management Business?

Over the last couple of years, new developments in technology to spur the monumental shift. First, almost overnight, the pandemic made digital communication the primary way clients contact and communicate with their wealth managers and advisers. Then, wealth business s became busy accelerating their cloud migration journeys, seeking intuitive data solutions to unlock advanced analytics and power up personalized client offerings. Later, to create better accessibility to third-party markets finished the trip of financial advisers to the recipe for AI-powered wealth management.

Don't ask, "What can you do for AI in wealth management?". Ask, "What can AI in wealth management do for you!" Or "Why use AI?"

To date, "AI-powered wealth management" constitutes utilizing advanced statistical models and machine learning magic to "digest" large amounts of customer and market data. This approach significantly increases prediction accuracy, automates back-office tasks, and generates more leads.

Let's recall how this landscape has developed over the past decade to fully understand why now is the best possible time for companies to reinforce themselves with the power of AI in wealth management. Statista shows that from 2009 to 2020, the wealth management landscape grew from $45.6 to $103.1 trillion, doubling the value of its assets. This can be due to low-cost products finally gaining market share or middle-class affluence growing. Plus, the developing economies' eventually shifted from addressing "the needs" to satisfy "the wants." Finally, let's not forget unlike many other business landscapes, wealth management as an industry has grown by 11% despite the global pandemic.

Practical aspect: The use of AI in actual wealth management

It's safe to say that artificial intelligence is the most discussed technology across multiple industries. The amount of buzz around this technology doesn't correlate with the actual pace of its adoption. And while wealth management businesses have long been aware of AI's capabilities, most firms are still uncertain if the complex nature of the transition is worth the risk. However, the need for change in wealth management has reached its apogee. With current customer demand for digitized experiences and fee reduction, along with intense competition and the steady stream of new investment opportunities, sector representatives need to find new ways of engaging clients. (Also, to generate leads, optimize workflow, and find ways to stand out in the market.)

To add an insult to injury, most operations forcefully went digital due to the pandemic. So many businesses struggle not only to find new clients and retain existing ones. Accenture's report shows that 55% of wealth management firms expect less economic stability this year. In this context, AI would be a great tool for wealth management businesses to address sudden short-term market shifts and still keep track of long-term opportunities. As a bonus, this creates more tailored and engaging experiences for clients while enabling wealth managers to make better-informed decisions and respond more to clients at scale.

And here are the five key ways AI will enhance your performance without the need to dope.

1. Hyper-personalization

Good news, everyone! Automation wealth management finally doesn't entail a complete loss of personalization. In fact, it can do the opposite. Today Machine learning-powered CRM can give advisers a far greater understanding of their Client's needs. Including preferences and attitudes. To the point of providing a level of detail you may not get it in person.

This enables managers to create unique client personas that go far beyond traditional wealth segments, creating customized user experiences. Just like the Netflix homepage is customized to your specific viewing habits and genre preferences, personalization can and will be the ultimate selling point for you and your firm. And it is delivered from the moment when clients log in to check their portfolios.

2. Well-informed decision-making and recommendations

Robo-advice is not at all a "new" feature/ However, in the past, it has indulged the mass market with limited scope for personalization. Using machine learning to give advisers the "Next Best Action" recommendations allows a new level of personalized advice tailored to individual client portfolios.

3. Level up the financial planning

Planning tax, trusts, and cash flow are considered more complex areas of wealth management. In the past, you most definitely needed to go to a dedicated financial expert. Who would provide you with a vast document and best case - suggest an industry provider to help implement it. Now, digital planning engines can perform these complex calculations. More so, these tech advances allow the Client to get a complete end-to-end battle plan (like the strategy with specific goals and recommendations to meet these goals.) Furthermore, you can get step-by-step micro-plans for a tree of different scenarios, not just a retirement plan.

4. Enriched model portfolios

For the last couple of decades, the "model portfolio" has been the bedrock of the wealth management landscape. There were six "uniformed" portfolios. It was considered enough to meet different risks, customer profiles, and "investment horizons." However, as ESG has evolved along with the investor's priorities, this methodology has failed the test of time.

To clarify, ESG can mean a multitude of different things. For example, there are seventeen "Sustainable Development Goals" to choose from. You can care more for clean water than poverty relief or equality. What we call "Model Portfolios," as they are today, simply cannot account for all of these individual preferences. But AI-powered algorithms can. AI can and will add to the dynamic growth of wealth management entities to provide you with personalized portfolio options with an optimal impact against those individual criteria.

5. Must-have of any modern business: real-time monitoring

Today any sane person is significantly more "hands-on" with their investments than even five years ago. Your current Client will no longer be satisfied with monthly portfolio updates. Instead, they (understandably and lawfully) want to track their instruments personally and stay in tune with any relevant landscape shifts or news in the market. This immediacy of first-hand access (to everything: the research, the market data, and the market news) allows real-time monitoring for clients and financial advisers. For example, your Client gets an alert if a market correction occurs or relevant trading signals are detected. You – the adviser – are notified that your Client received new data. This symmetry

The main benefit of using AI in wealth management

Artificial intelligence is smoking hot tech for financial enterprises of any kind. Here are the grouped-by-category benefits of powering your business with AI:

• Customer-centric solutions: Personalized, data-driven experiences for clients, starting with seamless digital onboarding. It is a serious selling point and a customer magnet. It aims to compensate for the lack of personal relationships with an adviser within the modern pace of life.
• Real-time intelligence for advisers (discussed above) aims not to exclude advisers but to help you and your Client make better-informed decisions by staying "on top of things" all the time without the need to fish out for info in the morning papers.
• Predictive analytics is the next hottest thing in tech, and it applies perfectly to investment advice. AI allows wealth managers to come closer to selling outcomes, rather than "model portfolios." not traditional investment products.
• Streamlined lead generation: With the power of AI, businesses can analyze vast amounts of publicly available data and accurately segment their prospects to have a better "palette" of new clients to service.
• Improved personalization: You can tailor your investment offerings based on the customer's unique needs. And AI will speed up sorting the data to pinpoint those individual needs. Plus, it will enhance customer engagement.
• Next-level automation wealth management: This point is not new, but it is very useful. Automate the time-consuming, routine tasks and processes to AI-powered systems, and your employees' time can be spent on cognitively demanding and more important tasks.
• Streamlined compliance management: To keep your hand on the pulse of rapidly changing industry regulations and legal requirements, AI systems will process every change in regulatory requirements from an unquantifiable number of sources at incredible speed.
• Improved decision-making: Wealth management businesses can utilize AI platforms to gain more profound insight into customer data and market fluctuations. This can enable a noticeable shift toward more effective decision-making.

Insights: Successful AI use cases within the wealth management landscape

AI can solve most wealth managers' challenges (banking manual work automation wealth management, ML-based fraud detection, or personalized market forecast). However, it requires a carefully tuned data model and certain data quality. At this point in time, 78% of financial institutions and wealth management entities already deploy Client and advisory-facing AI-powered technology. Usually, it becomes a test of their digital capabilities (and a "catch-up" for the 20% who are still out of the game). So let's see how AI can optimize wealth managers' workflow efficiency and drive their revenue.

Lead generation

Before the evolution of augmented analytics and artificial intelligence, to find potential clients, all the wealth managers on the planet had to rely on manual data analysis (and acquisition.) This way, all the decisions were based on several "Traditional" metrics like demographics and net worth. AI allows wealth managers to micro-segment their prospects based on more accurate data and a wider range of data sources (including social media, niche news stories, and multiple public data sources.) This new way gives you access to new robust leads and tailored pitches to those leads. More so, a well-calibrated AI system can help companies connect prospects to relationship managers who share the same interests (occupy the same age group, have had similar clients, etc.)

EXAMPLE: Finantix is a California-based financial technology provider. They created an AI-powered technology that can mine LinkedIn data. This tech alerts the manager if the potential lead is already connected to them. It also generates a pitch message in the appropriate tone. AI-driven technologies like NLP process large amounts of structured and unstructured customer data. It "optimizes" even the conversations based on details from perspective clients' profiles.

Establishing customer relationships

Focusing on the Client and their experience is the best invention of marketers and the best side-effect of the globalization and total digitalization of the world. Fostering meaningful connections with your clients is the key to success and not only in wealth management and financial advisory. The era of the Client has begun, and the clients demand an increasingly wider range of services, hyper-personalized financial guidance, and flawless user experience. With AI-powered employee-centric algorithms for Robo-advisors, wealth managers can fully satisfy their customers' needs.

Example: The Morgan Stanley Wealth Management Unit developed the "Next Best Action" system. It is designed to help fin advisors match Client's investment capabilities to their profiles. The system's AI algorithm allows advisors to speed up the generation of investment offerings and enhances its precision. Additional value of this system is hidden in its ability to identify clients' topics of interest, thus improving customer engagement.

Another famous example is Robinhood, the infamous online trading platform. It is famous because they put zero commission pricing models as its unique selling point. In the vast of the wealth management landscape, use of flat-fee models requires a granular understanding of clients’ profiles and exceptionally accurate forecasts of returns on their investments. Plus, a tremendously carefully tuned predictive algorithm, that would help detect clients with a high attrition probability. This way, enterprises can pinpoint such clients’ pains and make “preemptive strike” to ensure they stay with the company.

Financial advisory automation wealth management

In 2020, during the worldwide lockdown, ML-infused stock market analysis tools and Robo-advisory platforms became "the IT" of the financial world (minimizing physical interaction.)

Example: Wealthfront is an automated investment service. Amidst the pandemic, they have reported a 68% growth in account sign-ups. Wealthfront's Robo-advisory platform provides digital-only investment management and financial planning services. Wealthfront's end-to-end decision-making automation wealth management has generated interest in the past. However, they didn't earn clients' trust until the 2021 adjustments to its Robo-advisory platform that placed significantly more control in the hands of investors.

Back-office automation wealth management

McKinsey research showed that up to 70% of relationship managers' time, on average, is spent on "advisory-irrelevant activities." This is understandable - wealth management companies rely heavily on manual data analysis for risk and compliance management, asset recommendations, and, once again – lead generation. By adopting AI, companies can automate repetitive and time-consuming back-office operations. This, in turn, gives managers time and space to focus on more value-adding activities.

Example: KYC's traditionally manual research and compliance management checks approach is notoriously cumbersome and error-prone. They've turned to AI-powered data-extraction tool Magic Deep Sight to alleviate the inefficiencies. It gave KYC a 70% reduction in manual data analysis costs. In addition, AI-powered tools can easily be applied to automate invoice processing, reconciliation, and even fund accounting.

Compliance management

Regulatory bodies create and continuously update rules, standards, and regulations in the financial sector. Financial institutions, including wealth management businesses, must abide by those rules. Failing in compliance management may result in severely large fines and an extremely damaged reputation (which, let's face it, mostly determines the organization's well-being.)

Traditionally, a living, breathing team manually sifts through tons of regulatory documents and addendums to ensure firms' fitness and compliance management with various rules and standards. This process, while absolutely vital, is severely time-consuming and sadly largely ineffective. Luckily, modern advancements in digital tech, like AI, NLP, and advanced data analytics, are designed to free asset managers from routine tasks. Instead, they are designed to help compliance management be more efficient.

EXAMPLE: Ernst & Young built a cloud-based AI solution to extract the most important information from governing contracts and automatically detect liabilities. While "SARGE" is not entirely automatic, EY noted a 75% cut in compliance management teams' time.

Double, double toil and trouble: Possible AI implementation and their solutions

Despite the undeniable advantages of AI and the tremendous potential of AI in wealth management, to date, just a few companies have applied this remarkable technology at scale and made it a seamless part of their enterprise. So let's see which two obstacles you should consider when venturing into the AI adoption territory.

Data governance standard

sA PwC study of AI in wealth management landscape shows that businesses themselves are reluctant to scale AI. This hesitation is based on a lack of understanding of the technology and its reliability. Though understandable (data privacy remains a top concern in wealth management) and amplified by rigid regulatory requirements, this sentiment becomes a real obstacle. A poorly tuned AI model may indeed create more risks than opportunities. Remember, AI model output is only as good as the data fed into it. Thus, the efficiency and success of AI usage inevitably correlate with the level of "maturity" of data management infrastructure. Therefore, the way to avoid any blowbacks is to ensure your data accuracy and accessibility. Plus, be sure that data sourcing and analyzing processes comply with regulatory requirements.

Be prepared and prepare your workforce

The second major concern after AI reliability and data privacy is the readiness of the workforce to implement the tech daily. As the tech is relevantly new, current employee retraining and "change management" can be challenging in an AI adoption journey. Thus, it's most important to alert your workforce about the changes coming as early as possible. As a solution, assembling multidisciplinary teams for AI projects can be effective. Besides, such a move makes a company's strategic intent clear to the rest of the units. Another pro-tip is to start with AI use cases that can demonstrate the AIs' real-life value.

Headhunting and "internal reskill"

The next real challenge is the skill gap and talent recruiting. First things first: reskilling should never be an afterthought. Recruiting new talent and developing training programs for existing employees is important. As a precursor to success, identifying the missing roles as early as possible is the best possible move. Coming to terms with the stats that show - there is a shortage of AI talent will allow you to create a solid long-term talent strategy. Experts claim that this step is critical to benefit from AI in wealth management. Plus, recruiting new hires can be challenging because the most fitting candidates need domain-specific knowledge in tech and finance.

Continuously update risk frameworks

Operational and regulatory risks are not domain-specific. Nevertheless, the successful adoption of AI within wealth management involves a range of those. On the upside, it's easily fixable and prevented. Remember to update oversight procedures, including the IT department, as early as possible, and you will ensure critical errors are identified beforehand. Making sure that the AI model produces accurate results simply takes continuous validation. Routinely review models, look for potential bias, double-check input data, look for unintentional errors, etc. In case you're using a decision-making engine, it's especially important to model uncommon market scenarios. This will ensure the continued reliability of the system.

Conclusions: What is the future of AI in wealth management?

The coming years promise to be an exciting period of innovation in wealth management. About $78 trillion of assets in motion emerged because of the global expansion of the middle class, women and entrepreneurs, and business ownership. However, seizing this opportunity requires change. And the "AI revolution" is just the shift in strategy and approach that dictates the future. Basically, it's a unique opportunity to capture more value with AI-powered software.

Most of these solutions become a competitive differentiator, culture and mindset finally shift, and wealth managers realize that the "digital plain" is crucial for the future of their businesses. It is obvious by now that AI is the key to providing personalized online experiences the modern Client desires and requires. Plus, real-life cases prove AI helps businesses achieve greater efficiencies at scale. The apparent conclusion is that AI is worth investing in.

Let's explore the dynamics of the shift with the Accenture survey of 100 AI strategies among wealth management businesses. It shows that most wealth managers see a real opportunity to adopt AI over the next few years. Sadly, about 80% of companies get stuck in their tracks in the proof of concept stage. The sheer inability to scale is a profound obstacle at this time. To truly "unleash" the full power of AI on the wealth management landscape and gain a viable upper hand, businesses should accelerate, using real-life cases of wealth managers who seamlessly moved from theory to execution and gained benefits realized from the scale.

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