The global wealth management industry, since the advent of digitization, has been experiencing transformation. Robo-advisors are one of the pioneering examples of alternative business models that have surfaced during the last decade.
Capturing a White Space in Market that is Expanding
Traditionally, wealth managers have predominantly focused on the segments of high and ultra-high net worth individuals (HNIs and UHNIs). A large volume of individual investors with net investible assets less than 1 million dollars, known as the mass affluent segment in wealth management industry, remained largely underserved with less than 25% of existing institutions focused towards serving them. Robo-advisors have successfully tapped this segment in a big way. This key customer segment, comprising millennials (born after 1980) who are highly technology-savvy, hands-on, and extremely price-sensitive toward the professional portfolio management fees, is slated to grow in significance as they are expected to inherit the wealth from baby-boomers. Incidentally, it’s interesting to note that the average age of wealth advisors working for traditional players is above 50 years.
Robo-Advisors are on a Growth Spree with a Growing Market Segment
Robo-advisors leverage digital technology to establish a direct-to-customer business model, offering a simple and interactive user interface with a robust and integrated investment advice model. However, despite the growth of the global AUM of robo-advisors in excess of 100% per year, their projected market share of global wealth management AUM by 2021 is surprisingly expected to be less than 10%. This underlines the potential for this exciting business model for the wealth management industry.
Key Trajectories of AI-led Robo-Advisors
The recent burst of advances in Artificial Intelligence (AI) and Machine Learning (ML) hold a great promise toward quicker expansion of the global footprint of robo-advisors. The three important aspects of the robo-advisor business model that AI and ML can positively impact are:
- Portfolio management
- Investor Experience
#1 Portfolio Management
Machine learning enhances the analytics engine at the heart of robo-advisors to become more sophisticated, flexible and robust, over time. Sophisticated models based on deep learning will evolve into stronger automated investment portfolio management models that are effective in bearish as well as highly volatile markets—situations where traditional algorithmic trading is yet to be proven.
Investment managers have so far been accustomed to expert systems that have been useful, but largely rule-based and static in nature. The dynamic non-linear nature of financial markets and the unstructured nature of the investment decision-making process in several asset classes lend themselves very well to Artificial Neural Network based models. Other emerging technologies such as hybrid neuro-fuzzy systems including Adaptive NFIS, Gaussian RBF, and neural based Q-learning are also ready to hit it big.
#2 Investor Experience
Natural language process frameworks, including those for natural language generation, have started to become ‘intelligent’ and accessible to the developer community. Digital channels, including text and voice, that are integral to robo-advisor solutions will immensely gain from the cognitive ability of these computing techniques. They will help in better understanding of the human (investor) inputs and delivering emotionally empathetic experiences. It’s upbeat to note that these technologies are being increasingly proliferated in a very democratic fashion so that their increased accessibility and decreased acquisition costs will further strengthen the value proposition of robo-advisors.
The global financial crisis of 2007-08 affected investors with extreme distrust. Most investors saw no justification in the fees paid, against the shrinking returns from portfolios. The financial crisis also exposed financial institutions for their excessive risk-exposure and ineffective strategies for mitigation and hedging. The regulators introduced significant regulations to promote transparency and prevent the recurrence of such a global financial systems meltdown. These regulations require comprehensive and high-quality data across all phases of trade life cycle. Machine learning and AI techniques facilitate the collection, processing, and analysis of large volumes of structured and unstructured data from various internal and external data sources, providing greater levels of transparency to investors and for regulatory purposes.
There’s something to be said for any 100% algorithm-driven investment approach as its effectiveness is yet to be proven across the breadth of financial asset classes and product types. Also, the potential benefits of AI-led robo-advisors will not offset all the challenges that lie ahead, yet their pragmatic application of AI and Machine Learning blended with digital innovation could help them scale the business and realize the potential that many in industry believe this model has.