Holger Boschke
Chairman of TME
Along with blockchain, AI is one of the most popular words in banking and is likely to reshape the way financial services are presented today. But what exactly is artificial intelligence? How can AI be applied to wealth management?
What are its risks? Can it replace human intelligence?
What is artificial intelligence
In 1955, American computer scientist John McCarthy proposed the concept of artificial intelligence, arguing that "in principle, every field of learning, every aspect of intelligence can be described very precisely, so that a machine can be built to simulate it."
Other concepts such as machine learning, intelligent automation, cognitive computing, and self-service analytics are closely related to artificial intelligence. What these concepts have in common is that they all have the ability to learn large amounts of complex, unstructured (real-time) data, enabling computers to read, write, speak, hear, see, and interpret data. To take advantage of this capability, AI platforms need access to massive amounts of high-quality data related to routine transactions. The main factors driving the big data revolution are the amount of data, the speed of data processing, and the diversity of data types.
At the same time, the value and validity of data still exist throughout the wealth management industry. Tech giants like Amazon, Apple, Facebook, and Google have been using AI technology for some time. In contrast, banks have been very slow to use AI. The field of artificial intelligence is beginning to bring more and more practical benefits.
Marketing is a big deal, and many companies (such as Blackstone, Deutsche Bank, UBS, and Wells Fargo) are using AI engines to analyze people's digital footprints and behaviors and predict which products and services people are most likely to want to buy.
RegTech's solutions can also improve risk management, helping banks understand customers, regulatory reporting (e.g. MFD2), risk scoring, speculation, and more, and use this information for marketing.
In addition, AI can also help customers reduce operational risk by improving client security and using biometrics such as haptic recognition, facial and iris recognition, and even heartbeat recognition through wearable devices.
How to use artificial intelligence in wealth management
While these examples are all relevant to more efficient and effective processes, from the customer's perspective, they value elements such as better (i.e. faster, more objective and more personalized entry into cheaper (less expensive to automate) and more convenient (anytime, anywhere). While robo-advisors are a good example of how digitalization can improve financial services, the main components of wealth management require a lot of use of AI.
Customer onboarding and customer profiling
Customer profiling systems can perform specific personality analysis through behavioral analysis, such as asking customers simple, visual questions or using speech recognition software to assess a customer's risk appetite.
To help financial advisors better understand their customers, some banks have begun using AI systems, such as IBM's use of Watson (the supercomputer that won the "Jeopardy" competition) and chatbots to create financial profiles that provide users with personalized trading strategies.
Blackstone, the world's largest asset manager, has built its own artificial intelligence engine, Aladdin, which is also being used by a number of other financial service providers that can (and not) run certain "what-if" analyses.
These tools will become increasingly powerful, eventually enabling the simulation of relevant scenarios for a variety of different portfolios to facilitate better customer interaction.
Portfolio building and management
AI has been used in portfolio management for a long time and will play an even bigger role in the future.
High-frequency trading, sentiment analysis, portfolio optimization network analysis, etc., although there was earlier news such as Berkshire Hathaway, who is difficult to distinguish between actress Anne Hathaway and Warren Buffett, there are many other positive examples.
Much of the work that economists and analysts are currently doing will be replaced by AI-driven research platforms like Kensho. Goldman Sachs is using platforms like Kensho to analyze the relationship between historical data, market patterns and market dependencies.
Ultimately, this may even change the way we look at portfolio management. While there have been debates between fundamental and macro analysis, passive versus active investing, this time AI will replace modern portfolio theory with something entirely new.
Communication and reporting
Financial firms have a strong team of research analysts, strategists, and portfolio managers, but they all struggle to deliver their output to clients in a way that is both relevant and convenient. Artificial intelligence will play an important role in changing the way financial information is disseminated, using voice or visual processing technology.
Not only will chatbots be used as a primary element in automated customer interactions, providing customers with up-to-date insights and performance reports, but will also provide alerts for personal portfolios and assets.
By providing an integrated wealth view, financial services firms will be able to provide comprehensive advice while gaining valuable marketing options on funds held elsewhere. PSD2 already exists in payments, and in wealth management, it may be a few more years before we see something similar, but (mandatory) opening APIs for portfolios could disrupt existing market structures.
What are the risks of using AI
When it comes to AI and automated processes, there are always concerns about systemic risk and over-reliance on computer models. After all, algorithmic trading programs have caused asset prices to fluctuate sharply and plummet momentarily in the past, such as the quantitative pricing model invented by Wall Street in the early 21st century, which proved to be a typical failure model based on subprime mortgages.
In order to prevent similar situations from happening again, the first task should be to validate the machine's thinking through endless test scenarios and set the necessary restrictions and abort conditions. For example, the debate about self-driving cars, because self-driving cars are not 100% safe (they never will be), and many of the technologies used in driver assistance systems (which are already used to prevent accidents) are also considered to be up for consideration.
To solve this problem, companies need to decide how and to what extent AI will be used, depending on the work involved: it is the only way to make better, faster, and more informed decisions, or simply use it to oversee the work done by humans (or vice versa), or make delegated decisions.
Will artificial intelligence replace human intelligence?
Consumption is a very emotional thing and sometimes completely irrational. Financial advisors still have an edge over machines when it comes to providing emotional support and dealing with emotions.
At the same time, many financial advisors struggle to have the information they need in order to provide good advice to their clients, and they spend a lot of time getting it. However, using AI allows them to save time and focus more on other aspects such as customer relationships.
In addition, AI and automation were considered impersonal enough, but people can now easily overturn this argument. Because AI has proven to be more personalized in its recommendations than many of the standardized solutions offered so far.
Therefore, only companies that focus on individuality and use new technologies with empathy, or use them in conjunction with client advisors, can realize the full potential of new technologies.
conclusion
AI will play a huge role in helping wealth managers better communicate and articulate their value propositions, while combining them with the best CRM, portfolio and risk management tools to provide better service and more informed advice to clients.
The ability to integrate and use AI will be a key factor in a company's competitiveness over the next five years. At the same time, new technologies will attract new market players, making some banks look more like T providers, while others will become product innovators by leveraging artificial intelligence.
As part of the new service model, AI needs to redesign existing processes and adapt to the entire digital business strategy. It also requires a clear understanding of product and design features, and how to acquire them. If there's one thing we can learn from current experience, it's that improved technology in financial services won't be a limiting factor for the industry. Even in banking, data will become a new currency and become popular.
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