How will Robo Advisory improve with advancements in AI and ML?
According to Statista, assets under management (AUM) for robo advisors will have surpassed $2.76 trillion as of 2023, and a Deloitte analysis anticipates it to reach $16 trillion by 2025. Considering last year’s promising returns of 47% y-o-y, banks have started integrating intelligent systems and testing the integration of artificial intelligence (AI) applications in the investment advisory process.
Numerous factors, such as the increased acceptance of online investing, the rising expense of conventional financial advice, and the rising desire for individualized investment services, are fueling the expansion of the robo-advising sector.
Robo advisors (RAs) are posing serious competition to traditional financial advisors, forcing some of them to reduce their fees or introduce new digital services such as auto invest ETFs (Exchange Traded Funds) to compete.
- Greater transparency for investors and regulators alike will be made possible by improving the analysis of big data.
- Various countries’ governments have been teaming up with private banks, encouraged by the business impact of AI-based investment applications.
- RAs can eventually help control the dwindling global financial system as disclosure of risk exposure will be automated and risk mitigation efforts will be auditable.
Let us have a look at the factors affecting the RA model and the ways its algorithm is expected to be augmented in due course to weaponize its service.
Analysis of big data
RAs rely heavily on analysis based on Modern Portfolio Theory (MPT), which is a little mundane as it merely considers the mathematical parameters for investment decisions. But soon, AI-based models will help make financial advisory services hyper-personalized, providing a more intuitive service through a holistic wealth management app.
Artificial neural network-based models are especially well suited to the dynamic, non-linear character of financial markets and the unstructured nature of the decision-making process for investments.
- Finding a means to improve the quality of predictive data obtained from big data is the first step in realizing the full potential of AI and ML (machine learning).
- The abundance of real-time data relating to user behavior and financial patterns will prove highly useful in advanced AI-based portfolio management.
- Once the deep learning capabilities of AI are reached, it will not only be able to allocate funds according to specific client parameters but also uncover vast opportunities for diversification.
- Using the deep learning technique, AI models may be trained on a sizable data set and then used to solve an investor’s specific problem by building on the fundamentals rather than having to start from scratch.
- These insights can be used to determine a client’s risk tolerance, assist in establishing financial goals, and form investment strategies accordingly.
The rapid and democratic expansion of RAs will boost accessibility and lower user acquisition costs. All of this will bring in more user data for training the continuously improving model, strengthening the value proposition of robo-advisors.
Self-learning algorithms
The greater adoption of RAs will help steepen the learning curve of the algorithm, making the advice even better. This is possible because the RAs are built on predictive modeling.
A predictive model is implemented using a variety of machine learning methods, including gradient boosting, regression trees, random forests, and neuro-fuzzy systems.
Neuro-fuzzy systems, which take their cues from the human brain, can be used for a wide range of issues, including financial forecasting.
- As more sophisticated neuro-fuzzy systems become available, decision-making that is similar to that of humans will eventually be used in robo-advisory.
- These applications will not only include highly precise, quality data but also run on an algorithm that will be extremely malleable.
- This means that it will be able to learn and unlearn different concepts with changing trends, unlike humans, who are wrought with biases.
- All this will happen in real-time, making robo-advisory all the more irresistible.
Furthermore, risk-accurate investment options can be provided if the system’s user segmentation is based on joint profiling, demonstrating the benefits of intelligent financial innovations for both customers and banks.
- To construct a more thorough profile of a user, the process of “joint profiling” involves merging data from many sources.
- Demographic, financial, and behavioral data are some examples of this data.
- Banks can better understand the requirements and interests of their clients by merging this data.
- More specialized financial goods and services can be created using this information.
Advisory chatbots
Today, automated communications between banks and customers are made possible by processing natural language in conversational AI-based chatbots and voice assistants.
Robotic advisers are accessible from anywhere with an internet connection and are available 24/7.
- They are therefore perfect for those who are constantly on the go or don’t have the time to meet with an investment account manager face-to-face.
- The high level of mobility will offer this engagement channel a significant advantage over other channels in the future.
For the customer to communicate with the bank whenever and wherever they choose, robo- conversational agents will be implementable across a variety of hardware and software platforms by evaluating risk preferences or even analyzing customer behavior.
- For digital questionnaires, response switching might be a concern since it can make it challenging to collect reliable data.
- Response switching in the context of robo-advisors may indicate that a customer is unsure of their risk tolerance or investment objectives.
A robo-advisor that can better understand a client’s needs and offer individualized financial recommendations is the ultimate goal of all these innovative undertakings.
- RAs will automatically segment the market and its clients into functional (micro) categories and give their investors options to choose from or decide for themselves if needed.
- Neo-banks have started offering intelligent chatbot services to their clients, as the human-machine interaction at the client-bank interface provides seamless client journeys.
Preventing communication gaps between solutions, robo-advisors, and clients will intensify confidence in RAs.
The Era of Automated Investment Advice
The financial markets are getting more difficult to understand, making it challenging for ordinary investors to make informed decisions about their investments. RAs help simplify this humongous task through various educational resources and expert advice incorporated into an automatic investment plan.
Robo-advisory has great potential to revolutionize the financial sector, provided we find a sustainable way to inculcate a purposeful collaboration between the human brain and artificial intelligence.
Learning to incorporate the highly complex decision-making of professional investors into AI-based processes will aid in improving the understanding of investor inputs and providing empathetic experiences.
Rest assured, investors will have a veto in sequential decision-making between investors and AI systems, ensuring ultimate authority.
According to a recent poll, 80% of banks intend to utilize intelligent technologies for customized investments more frequently during the next three years.
The sector is still in the early stages of growth, necessitating more testing and review of the AI-led investment counselling process.
Nevertheless, robo-advisory will undoubtedly advance due to technological prowess, deepening financial inclusion in society. The more customizable it is, the more value it adds to individual investors.
