At this point, it's safe to say that artificial intelligence holds the title of the most discussed technology across industries. At the same time, the technology's popularity doesn't correlate with the pace of its adoption. While wealth management firms have long been aware of the possibilities AI offers, most of them are still uncertain if the game is worth the candle.
However, the need for change in the wealth management sector has reached its climax. Faced with intense competition, increasing customer demand for digitized experiences and fee reduction, and the avalanche of new investment opportunities, companies need to find new ways of engaging clients, generating leads, optimizing work, and standing out in the market. On top of that, with the majority of operations forcefully going digital due to the pandemic, many firms are struggling to find new clients and retain existing ones.
The recent BFSI digitization has prepared the ground for wealth management companies to enlist the help of AI experts and head confidently from technology exploration and pilot projects to company-wide implementation.Â
What is AI in wealth management?
AI in wealth management means utilizing machine learning and advanced statistical models to process large amounts of customer and market data to increase prediction accuracy, generate more leads, and automate back-office tasks.Â
Why use AI in wealth management?
To understand why now is the perfect time for wealth management companies to reinvent themselves, let's recall how this sector has developed over the past decade. According to Statista, from 2009 to 2020, the wealth management sector doubled the value of its assets, growing from $45.6 to $103.1 trillion.
This can be explained by low-cost products finally gaining market share, growing middle-class affluence, and developing economies' shift from addressing needs to satisfying wants. It's worth noting that, unlike many other business sectors, the wealth management industry has managed to grow by 11% despite the global pandemic.
However, extracting value from these opportunities is a great challenge. According to the 2020 Accenture report, 55% of wealth management companies expect less economic stability. In this context, wealth management firms need to be able to address sudden short-term market shifts while also keeping track of long-term opportunities.
5 benefits of AI in wealth management
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1 |
Streamlined lead generation |
By analyzing huge amounts of publicly available data, organizations can accurately segment their prospects and have a better chance of winning new clients. |
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Improved personalization |
By quickly identifying each customer’s unique needs, wealth management can tailor their investment offerings and improve customer engagement overall. |
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Enhanced automation |
By allocating routine tasks and time-consuming processes to AI-based systems, companies can free employees' time for more important and cognitively-demanding tasks. |
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4 |
Streamlined compliance |
Modern AI systems can process regulatory information from a myriad of sources at a lightning-fast speed, which ensures that companies can stay on top of rapidly changing regulatory requirements. |
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5 |
Improved decision-making |
AI platforms allow wealth management firms to get a much deeper insight into customer and market data, enabling substantially more effective decision-making. |
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6 most impactful AI use cases with examples
Whether it’s market forecast, banking personalization, manual work automation, or fraud detection with machine learning tools, with a carefully tuned model architecture and sufficient data quality, AI can solve the majority of wealth managers’ challenges. With 78% of organizations already deploying both client and advisory-facing AI-driven technology, it’s a serious test of their digital transformation capabilities and a catch-up game for the other 20%. Let’s discuss exactly how artificial intelligence can help wealth managers optimize workflow efficiency and drive more revenue.
1. Lead generation
Until the advent of augmented analytics and AI, wealth managers had to rely on manual data acquisition and analysis to find potential clients. In that case, decisions were mostly based on conventional metrics like client demographics and net worth. With AI, wealth managers can micro-segment their prospects based on a wider range of data sources including social media, niche news stories, and various public data sources, find new leads, and tailor pitches to them.
Furthermore, an AI system can help companies to connect prospects to relationship managers that share the same interests, are in the same age group, or have had similar clients in the past.
Example: Finantix
Finantix, a California-based financial technology provider, developed AI-driven technology that can mine LinkedIn data to see if the relationship manager is already connected to the potential client and generate a pitch message in the appropriate tone.
While it may seem that formulating an attractive offer is something that only humans can do, Chris Burke, Vice President at RBC Wealth Management, explains that AI-based technologies like NLP can process large amounts of both structured and unstructured customer data and quickly optimize conversations based on details from prospects' profiles. Clients’ risk tolerance can also be more accurately determined by assessing how their transactions change in response to market events. This way, the technology can significantly increase wealth management companies’ chances of winning new clients.
2. Fostering customer relationships
In the context of wealth management and financial advisory, establishing meaningful connections with your clients is the key to success. We have already entered the new era of clients demanding an increasingly wider range of services and hyper-personalized financial guidance underpinned by flawless user experience.
With AI-powered employee-facing Robo-advisory systems, wealth managers can predict what next actions are best in terms of satisfying customer needs. By delivering more meaningful and personalized communication, wealth management firms have a much higher chance of increasing customer loyalty and retaining clients long-term.
Example: Morgan Stanley
The Morgan Stanley Wealth Management Unit developed a Next Best Action system to help financial advisors match investment possibilities to client profiles.
Jeff McMillan, the company’s chief analytics officer reveals that the system’s advanced AI algorithm allows advisors to generate investment offerings much quicker and with greater precision. Importantly, McMillan said that the real value of such a system lies in its ability to identify clients’ topics of interest and enhance customer engagement.
The rapid influx of AI-based fintech also initiated a shift towards reduced fees of financial advisory, which calls for adjusting pricing models based on clients’ investment profiles rather than service quality.
Example: Robinhood
Another example is the online trading platform Robinhood that puts zero commission pricing models as its unique selling proposition. In a wealth management context, the implementation of flat-fee models requires a granular understanding of clients’ profiles and accurate forecasts of returns on their investments.
On top of that, a carefully tuned predictive analytics system can help detect clients with a high attrition probability. This way, firms can determine these clients’ pain points and take preemptive measures to make sure they stay with the company.
3. Financial advisory automation
In 2020, robo-advisory platforms and other tools to analyze the stock market with machine learning surged in popularity, which can mostly be attributed to the pandemic minimizing physical interaction and causing financial volatility.
Example: Wealthfron and Vanguard
California-based automated investment service Wealthfront reported a 68% growth in account sign-ups amidst the pandemic.
Notably, Wealthfront’s robo-advisory platform is among the few that provide digital-only financial planning and investment management services. Wealthfront’s underlying AI algorithm analyzes a client’s saving and spending patterns and automatically determines the optimal steps for reaching their financial goals.
End-to-end decision-making automation has generated a lot of interest throughout the years, but couldn’t really earn clients’ trust. This is why in 2021, Wealthfront decided to adjust its robo-advisory platform and put more control in the hands of investors to maintain long-term customer relationships.
Vanguard, on the other hand, also deployed an automated robo-advisor platform, but no actions are taken without the confirmation of managers and clients. Importantly, Vanguard has become one of the biggest players in the robo-advisor realm with over $221 billion in assets under its management. This largely favors our argument that in the wealth management context, AI shouldn't replace humans but assist them instead.
4. Back-office automation
According to recent McKinsey research, relationship managers spend up to 70% of their time on advisory-irrelevant activities. This is due to wealth management companies still relying on manual data analysis for asset recommendations, risk and compliance analytics, as well as lead generation.Â
By implementing AI, companies can automate many tedious and repetitive back-office operations, enabling managers to focus on more value-adding activities and become more productive.
Example: Magin Deepsight
The conventionally manual approach to KYC is notoriously error-prone, cumbersome, and inefficient. AI-powered data-extraction tools like Magic DeepSight allow for up to 70% reduction in costs associated with manual KYC data analysis. Similarly, AI tools can be applied to automate reconciliation, invoice processing, and fund accounting.Â
5. Compliance management
In the financial sector, regulatory bodies create and continuously update a set of rules and standards, that financial institutions, including wealth management firms, need to abide by. A failure to comply results for financial companies in extremely large fines and a severely damaged reputation, which can become detrimental to an organization's well-being.
Conventionally, a dedicated team of professionals manually sifts through regulatory documents to ensure compliance with a range of rules and standards. While absolutely vital, this process is largely ineffective and time-consuming. Luckily for wealth management firms, advancements in digital technologies tend to scale in line with the level of regulatory pressures that authorities put on the financial sector.
AI, NLP, and advanced data analytics are posed to liberate asset managers from routine tasks and make compliance management more efficient. For example, with the help of NLP, asset managers can quickly extract guidelines from investment management agreements or regulatory documents. Crucially, these AI systems can not only cut operational costs but also allow organizations to react to regulatory changes much quicker and thus increase their business resilience.
Example: EY’s SARGE
Ernst & Young built a cloud-based AI solution that allows wealth management firms to extract the most important information from governing contracts and automatically detect liabilities. SARGE is not entirely automatic, but EY estimates that the solution can save up to 75% of compliance management teams' time.
6. Sentiment analysis
In broad terms, sentiment analysis implies the interpretation of emotions from any text-based source, be it a news article, social media post, personal blog content, etc. With the help of natural language processing, wealth management companies can analyze public opinions on various topics, trends, events, and companies in real time, facilitating much more informed investment decisions.Â
In the past few years, Environmental, Social, and Governance investing strategies have become significantly more popular, which makes accurate and timely ESG insights as valuable as ever before. The sheer volume of data that is relevant to ESG and other investment strategies has become so large that an automated and intelligent sentiment analysis system is virtually the only way to analyze it in real-time.
On top of that, defining reliable ESG signals has always been a hard task. However, with AI, transforming qualitative text-based data into quantitative data and using it to make evidence-based investment decisions become much more attainable.
Example: MarketPsych
For example, MarketPsych Analytics is a provider of financial sentiment and ESG software that derives data from 4000+ news and social media outlets. The platform also tracks all major assets including bonds, currencies, commodities, and equities, covering more than 30,000 global companies.
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AI implementation challenges and their solutions
Despite the huge potential of AI in wealth management, only a few companies have been able to apply this technology at scale and make it a functional part of their enterprise. Let’s discuss four best practices for wealth management AI adoption.
Establish data governance standards
According to a recent PwC study of AI for asset and wealth managers, many firms are reluctant to scale AI because they aren’t sure about the technology’s reliability. This is understandable, since data privacy remains a top concern in the wealth management sector, further amplified by increasingly rigid regulatory requirements. Indeed, a poorly tuned AI model will most likely create more risks than opportunities.
Since the AI model output can be as good as the data fed into it, the success of AI initiatives strongly correlates with the level of maturity of the corporate data management infrastructure. Wealth management companies should ensure their data is accurate and accessible and that the processes of data sourcing and analyzing are aligned with regulatory requirements.
Unfortunately, given that many wealth management firms have relied on manual data collection for decades, gaps in client profile information and lavish amounts of unstructured data can create significant limitations. This can be further exacerbated by siloed data repositories and the absence of a unified data platform, for example, a data lake or a data fabric solution.
To overcome these challenges, companies need to take a step back and revamp their data governance frameworks. First and foremost, this implies developing data standards and glossaries, implementing quality assessment tools, and establishing data governance roles. Further down the line, it’s paramount to establish data governance policies and controls, reporting frameworks, and automated solutions for data reconciliation.
Prepare the workforce
Right after AI system reliability and data privacy concerns, most companies consider new talent recruiting, current employee retraining, and change management as their next most challenging AI adoption tasks.
Regardless of your company's AI adoption stage, it's important to let the workforce know about the incoming changes as early as possible. For example, assembling multi-disciplinary teams for AI projects makes a company's strategic intent clear to the rest of the organization. It's also important to start with AI use cases that are most feasible to demonstrate the technology’s real-life value.
For example, the aforementioned company Morgan Stanley, first started with a rule-based system to suggest investment offerings. Initially, this system had nothing to do with AI, but it showed where the company was heading and helped them to get quick returns from automation.
In general, back and middle-office automation use cases are a great starting point for the majority of wealth management companies. With early adopter firms already paving the way for AI-driven back-office automation, other companies can use them as an example and learn from their mistakes.
Recruit new talent and reskill internally
Workforce reskilling and talent recruiting should also never be an afterthought. It’s important to develop training programs and identify missing roles as early as possible. Given that there is currently a shortage of AI talent, it’s critical to develop a long-term talent strategy to fully benefit from AI in wealth management. Hiring new talent can be particularly difficult since candidates ideally need to have domain-specific knowledge in both technology and finance.
This is why companies should bridge the gap between IT and business development teams and first look for talent internally. Besides, here predictive analytics tools for HR may also come in handy, as such AI-powered solutions enable organizations to quickly evaluate their existing workforce and find and filter candidates with relevant domain expertise.
Update risk management frameworks
The path toward successful adoption of AI also involves a range of operational and regulatory risks that wealth management firms need to consider.Â
- Update oversight procedures and involve the IT department early to ensure that critical errors are identified in time.
- Ensuring that the model produces accurate results comes down to continuous validation. Organizations need to routinely review models for potential bias, check input data, look for errors, determine explainability scores, etc.
- Especially if it’s a decision-making engine, it’s important to model uncommon market scenarios to ensure that the system remains reliable in any situation.
- AI applications that automatically take decisions have significantly stricter regulatory requirements. Therefore, models' risk thresholds should be established not only from the business perspective but from a regulatory one as well. It’s important to remember that purchasing ready-made AI solutions or outsourcing development from third-party vendors doesn't relieve organizations of the responsibility to comply with respective laws and regulations.
Conclusion
Undeniably, artificial intelligence is every wealth manager's goldmine. While the competition from early AI adopters is rather tough, wealth management firms should not approach AI implementation head-on. AI calls for thorough preparation and, most importantly, for every part of the organization to be on the same page. To capitalize on opportunities offered by this technology, wealth management companies need to objectively assess their market position, long-term goals, and technological readiness and develop a detailed implementation roadmap.
In the end, those who manage to overcome initial barriers and make AI a vital part of their companies’ workflows have all the chances to end up as market leaders.
