Last year, global asset and wealth managers spent over $2.6 billion on artificial intelligence and machine learning tools. While already a massive number, Grand View Research expects this category to grow at a 25% compound annual growth rate (CAGR) through 2030.
Financial services has always been data-driven and analysis-heavy which is why it it is adopting AI at a faster pace that other business sectors. The synergy between AI/ML’s analytical capabilities and the research-intensive nature of asset and wealth management has driven considerable efficiency gains and are only scratching the surface of what can be achieved.
However, the benefits of AI/ML extend well beyond operational efficiency. Firms leverage these technologies to realize enhanced returns, mitigate risk, and revolutionize client communication methods.
A number of external factors are driving asset and wealth managers to reconsider their traditional business strategies. These include exponentially increasing data volumes, strict regulations, and rising interest rates. AI has the capability to reduce the impact of all of these factors, if it is leveraged effectively.
The assets under management (AUM) a firm can handle effectively is dependent on how efficient they are at serving their current client base as well as attracting new business.
The firm’s size also plays a part but is not the sole deciding factor. A large firm with cumbersome or inefficient processes leaves a lot of money on the table, while a small firm with optimized workflows handles far more AUM than other firms of a similar size.
Deloitte found that firms incorporating AI/ML into their processes can increase their AUM and productivity by 14%. This gives advisors more time to optimize their client’s portfolios for maximum returns.
We’ve already mentioned AI is perfect for analysis, but it’s equally good at summarizing those findings. Reports that used to take advisors hours or days are completed in minutes with the right prompts using AI and find hidden opportunities in the data that a manual review might miss.
These reports are more concise than a human would write and provide a solid basis to work from. Advisors can use the time saved for tasks such as deeper dives into client portfolios, additional client meetings or prospecting. And the reports received by clients will be more thorough, personalized, and relevant.
Even the SEC has adopted AI to expand its analytical and monitoring capabilities. The agency uses the technology to review trade data and match it to news events to detect potential insider trading patterns.
Called the Advanced Relational Trading Enforcement Metrics Investigation System, ARTEMIS analyzes supicious trading patterns, and then looks for relationships between those involved in those trades from publicly available data. Even though this involves billions of trade records, the model can search through themin a matter of minutes.
ARTEMIS has already shown its worth: in 2021, the system uncovered an insider trading scheme by Netflix engineers and has a successful track record extending back as early as 2016.
Deeper and more informed analysis results in better client returns because no stone is left unturned. Historically, advisors used regression techniques and models developed for decades to develop their recommendations.
BARRA, which was acquired and merged into MSCI in 2004, has some of the most sophisticated alpha prediction and risk analysis methodologies on the market. AI/ML’s predictive capability will eventually be better than BARRA’s, even though their work is based on more than two decades of experience.
Firms have leveraged AI to enhance trading operations, and it is already widely used for algorithmic trading. However, AI can be helpful for executing complex and large trades, timing and splitting them up to get the best price.
ING was one of the first to use AI extensively in trading. Its bond trading tool, Katana, first introduced in 2017, comes to a pricing decision faster than previous methods 90 percent of the time.
AI can also help firms reduce their risk exposure, which is one of the technology’s strongest use cases. Risk analysis requires the close study of a massive amount of data, from news and social media to market research and financial reports. Even with the assistance of technology, this takes time.
That isn’t the case with AI. It can quickly analyze large amounts of data, providing a much more current view of your firm’s risk exposure. And these analyses improve with time as the model ingests real-world data and compares it to its initial findings.
AI assists risk managers in validating and back testing risk models. AI approaches can also extract information more efficiently from various sources of structured or unstructured data and generate more accurate forecasts of bankruptcy and credit risk, market volatility, macroeconomic trends, financial crises, and so on than traditional techniques.
Cybersecurity is an excellent example of how AI helps to lower risk. There, IT departments often deal with threats that aren’t necessarily known. Detecting and responding to these situations is difficult because there are no guidelines.
But there are commonalities across most cyber threats. Antivirus software makers have developed AI models that detect those common warning signs and act to contain threats without human involvement quickly, even if the threat is unknown.
While we’re not dealing with the same nefarious threats in finance, there is the same problem of those “known unknowns.” Many events could affect your client’s portfolio and assets, some of which are out of your control or difficult to detect until it’s happening.
AI has proven to be extremely useful in these situations.
Communication between client and advisor is important to maintaining a successful relationship. However, the time required to research, produce, and present work to clients requires considerable time and effort.
Advisors deal with multiple clients, and their time for each is finite. Templates can help improve efficiency. While this might be fine for general analysis, each client is different in some way, which requires customization.
AI’s speed and analytic capabilities allow advisors to offer far more individualized communication. And more often than not, AI models are able to identify issues that advisors wouldn’t have found on their own.
Larger firms have also successfully deployed chatbots as part of their client communications strategy. JPMorgan developed a ChatGPT-like model called IndexGPT to help clients find investments that match well with their portfolios. Instead of creating a chatbot model alone, Morgan Stanley recently partnered with OpenAI and will use the company’s technology to make its extensive market research more easily accessible on-demand to its advisors.
An advisor would communicate with the chatbot in the same way people do with ChatGPT. It would be trained on Morgan Stanley’s data, and return answers based on that, along with links to any source documents for its answer.
The transformative power of AI is just beginning to be realized in wealth and asset management. Combining artificial intelligence and machine learning technologies with traditional financial analysis and portfolio management best practices can yields significant operational efficiency, enabling advisory firms to support more greater AUM and a greater number of clients.
AI’s analytical prowess not only streamlines the process of generating detailed reports but also empowers advisors to uncover hidden opportunities and deliver superior returns. Additionally, AI plays an instrumental role in risk mitigation, as it combs through vast data sets in real time, alerting advisors of potential threats and market volatilities that could impact their portfolios.
By relieving advisors of time-consuming tasks, AI allows them to offer their clients more personalized service, fostering stronger relationships. AI’s ability to offer high-speed, data-driven insights is revolutionizing client communication.
The financial sector is witnessing a paradigm shift driven by AI, and the potential of AI in portfolio management is immense. We are just scratching the surface of its capabilities, and much is yet to be learned.
One the earliest players in FinTech, SoftPak has developed innovative products at the intersection of business solutions and IT infrastructure since 1994. Our rule-based rebalancing and risk-based optimization software powers some of the largest financial institutions in the world, processing over $500 billion in assets. Headquartered in Massachusetts, SoftPak has offices worldwide.
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