Understanding the Power (and Limits) of AI in Asset & Wealth Management – Toolbox

Increased digitization in the financial sector has been made possible through recent breakthroughs in AI and ML. Maureen Doyle-Spare, GM of assets & wealth management at UST, provides a deeper analysis of the power that AI brings to the art and science of managing wealth and assets, as well as the limitations yet to be overcome.

All trends point to these technologies playing an ever-larger role in how financial professionals meet emerging demands. Technology once limited to large institutions has become more democratized and embraced across the financial service sector, fintech players, insurance firms and even regulators.

See More: Top 10 AI Development and Implementation Challenges

The Rising Demand for Artificial Intelligence

AI has emerged as a promising tool in this sector because of its ability to guide portfolio management, ensure regulatory compliance and streamline long-term financial planning. Similarly, Machine Learning has emerged as the ideal solution for financial monitoring, investment predictions and process automation.

With its ability to appraise information coming in from multiple sources, AI has found exciting use cases among fundamental investors through competitive analysis and in building advanced financial models. AI allows the incoming information to operate in siloed systems across multiple applications to be harnessed for efficient collaboration and become a feature of your data. As the technology improves and finds more widespread use, increasing AI customization also empowers clients to make better decisions.

The adoption of AI is now empowering Asset and Wealth Management firms to do things they couldn’t do before, such as augmenting the intelligence of the human workforce and facilitating the development of next-generation capabilities. AI enables these firms to dramatically deliver new value and reshape operating models by implementing use cases to facilitate alpha generation, manage risk, improve customer experience, and create efficiencies. 

Just like how your Netflix algorithm gets to know your viewing tastes to recommend the ideal programming, quant models offer investment professionals the ability to perform the statistical analysis necessary to present clients with the right options for their financial profiles. 

See More: What Is ML Bias and Where Can We See It?

Similarly, machine learning brings unprecedented analytical capabilities to vast data sets nearly incomprehensible to the human mind. The insights gleaned from combing this data can be applied to solve the increasingly complex problems that determine success in the modern financial sector.

Machine learning-based solutions and models also make algorithmic trading (AT) possible, allowing for rapid decision-making that is only possible by monitoring trade results and news in real-time and securing the best possible prices for investors. 

Adoption Obstacles

While these exciting technologies have enormous potential to bring the power of digital transformation to the financial sector, it is essential to remember that the technologies have limitations. However, this does not mean financial firms should turn all decision-making to automated algorithms. There are still limitations to these technologies, and people will always have a vital role to play – no matter how sophisticated technological solutions become. This means that firms looking to get the most out of AI and Machine Learning will need to take an honest look at their capabilities while understanding industry trends and knowing where to exercise restraint.

There is a danger in viewing AIs as purely rational and objective, but in reality, they reflect the inputs given to them by their programmers. Viewing AI as a catch-all solution is a recipe for disappointment and eventual losses. Financial firms can mitigate AI-related risk by performing comprehensive audits of their underlying processes and confirming that all information is up-to-date and reflects the latest market consensus. This includes market factors and potential regulations, market sentiment, and everything that governs risk in a globalized economy.

It is critical to understand the connection between AI and the data that powers it while leveraging the availability of curated data and the datasets that drive it to make the most of those new and meaningful insights and actions. 

A Winning Combination: Human-AI Collaboration

AI and ML solutions function best when complemented by human users who can guide these processes toward the highest possible productivity. Human oversight is necessary because algorithms will always identify a pattern – even when there is none. Because they interpret random coincidences as patterns, algorithms must be monitored so financial firms do not make strategic mistakes. This level of oversight and ability to identify possible errors requires comprehensive knowledge of the markets and well-trained financial professionals.

In addition, decisions made by AI can lack transparency since financial firms are reluctant to share the raw data and inputs that power their algorithms. While this may not present a problem in a bull market, when times are tough, and portfolios dwindle, clients may want to know how investment decisions are made. Firms that depend on AI and machine learning to guide investment decisions must articulate their underlying rationale. People who entrust their life savings to an investment company that relies on AI must understand the firm’s overall strategy and be given options that match the level of risk they are comfortable with. In short, these decisions should be treated the same as when human investors are calling the shots. 

AI and machine learning will continue to play a pivotal role in the democratization of finance in years to come, providing users with the agility to build upon their successes and meet emerging market trends. However, as these technologies become more commonplace, it will become more critical to ensure that they are applied correctly so that users can maximize value. 

How can obstacles to AI adoption be managed better? Tell us what you think on LinkedIn, Twitter, or Facebook. We’d love to know!

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