09/30/2022 | News release | Distributed by Public on 09/30/2022 01:01
Advances in artificial intelligence, and digitization in general, enable wealth managers to provide automated investment advice tailored to individual client needs. Prominent examples of this trend are the robo-advisors currently entering the market.
Implemented with a strong partner, AI creates new insights, generating a holistic picture of the client's life situation. This helps wealth managers identify the best matching investment product from the available range.
As technology advances so investor reluctance decreases, as shown by an Avaloq survey from 2021. In short, around 75% of investors surveyed said they'd be comfortable with AI-supported or AI-driven investment advice in the future.
Artificial intelligence
Figure 1 shows the AI capabilities most important to wealth management.
As AI's core capability, machine learning (ML) drove the recent advances in artificial intelligence. ML enables the training of AI algorithms using large data sets, ideally, without human intervention. And as such, it relies heavily on the availability of big data, improving natural language processing (NLP) and robotic process automation (RPA).
Figure 1: AI in wealth management
Machine learning
Depending on the type of training data, you need to distinguish between supervised and unsupervised learning.
Supervised learning relies on structured data and is mainly used for classification; two examples being the risk classification of stocks, and regression where the house price is a function of size.
Unsupervised learning can handle unlabeled and unstructured data like key-value pairs, log files or images. Its best use is for clustering data or identifying associations. Prominent examples of these are automated segmentation of the client base, or the identification of certain patterns in client activities, which could indicate a cross-selling opportunity.
Natural language processing
NLP benefits from recent improvements in deep-learning techniques, pushing chatbots and virtual assistants to the next level. Shifting from recurrent neural networks (RNN) to the transformer model, eliminated the sequential structure of RNNs and allowed for parallelization.
A famous example is the Generative Pre-trained Transformer 3 (GPT-3) created by OpenAI. GPT-3 was trained on hundreds of billions of words and based on 175 billion ML parameters; it's the largest non-sparse model to date. Given a few key words, it can write an entire email that sounds as if it's been written by a human.
Robotic process automation
Coupling robotic process automation with machine learning allows you to use RPA for processes with limited training data. Here, RPA escalates an issue to a human operator whenever the confidence of a decision is below a certain threshold. The result is fed back into the solution so that subsequent cases don't need escalating. This ensures that the quality of an automated process will improve over time.
Big data storage
Process automation
A prominent example is the chatbot which helps potential clients during the onboarding process and the execution of self-service processes.
Another example is the use of NLP for standard client interactions. Here, NLP can prewrite emails to answer recurring client requests.
When it comes to the extraction, validation and digitization of large volumes of data, RPA enables the automation of error detection and correction. As soon as the RPA algorithm detects an error it can't fix by itself, it notifies the appropriate person to fix the error and observes the remediation. Should this kind of error occur again, the algorithm will be able to fix it automatically.
Client segmentation
For example, analyzing all your client interactions, be they via email, mobile or messenger, AI algorithms can determine how easy it is to do business with them. This analysis could include the overall duration of the interaction as well as the style of communication. Ultimately, this approach allows individual predictions of client growth potential and a fact-based decision on the effort and time to invest in each client.
More importantly, client segmentation is not static and AI technologies allow for adjustments in near-real time (should the data show a significant change in the relationship on your client's side). Buying a house usually reduces the client's risk appetite in the short term.
Monitoring, alerting and cross-selling
For example, previously unnoticed and recurring payments for baby essentials would raise an alert to discuss fund savings plans for children. On the other hand, past behavior of leaving clients enables AI algorithms to identify clients with a high churn risk. This allows relationship managers to conduct a client meeting before he or she decides to leave.
Robo-advisory
For example, NLP can help with sentiment analysis of social media posts and analysis of annual reports or news sites. Neural networks can be used to predict stock returns and ML, to identify the most relevant variables for these predictions.
For institutional wealth managers, neural networks can also help replicate an index by holding a fraction of constituents and reducing portfolio management costs.
Overall, AI enables tailor-made proposals across all asset classes. Especially when it comes to securities, ML allows wealth managers to drop the standard one-size-fits-all approach. It can provide the RM with a selected list of opportunities in line with the risk appetite and personal situation of the client.
Find a partner to eliminate roadblocks
Your partner should also ensure AI projects adhere to the following core principles, which can reduce associated risks significantly:
Next step
Migration to an AI-based business model requires a continuous team effort. Ideally, the first step focuses on a hybrid model, with AI improving existing processes and making educated investment suggestions. Once the process is stable and advisors are comfortable using the system, full automation toward the client can be gradually introduced.
So, if individual investment advice is on your strategic road map, and you're getting a little concerned about how to actually deliver the business benefits, feel free to contact me. Luxoft can help you to make your AI strategy a reality.
About Luxoft
Martin Wackenhut
Director, Technology and Strategy Advisory, Luxoft
Originally a numerical physicist, Martin has been working in the insurance, automotive and banking industries for over 20 years. Having a proven track record as enterprise architect, head of IT architecture and interim CIO, he enjoys working hands-on with new technology and optimizing business processes.