Wealth Management and Artificial Intelligence

All things evolve. Wealth management is no exception. And it is currently at a turning point: changes seem all but inevitable.
All things evolve. Wealth management is no exception. And it is currently at a turning point: changes seem all but inevitable.
In fact, according to recent PwC predictions, AI (Artificial Intelligence) will drive an increase in global GDP of 14%. Just one of those irrelevant statistical numbers? Let’s sauce it up a bit: 14% of global GDP amounts to about $15.7 trillion!
No wonder the asset management field is expecting a revolution. From individual asset owners to institutions, AI is gradually becoming a powerful tool for managing data and dealing with risks.
And we are gradually becoming your favorite site on all things AI-related.
Let’s see what’s around the corner this time.
Applications of AI in Wealth Management
AI is a powerful tool. If we’re right, soon enough, it will be omnipresent: in power systems, in pharmaceutics, in the legal world.
In the case of wealth management, we’re already a step ahead. There are plenty of services and platforms which are used all around:
  • Aladdin helps managers analyse investment risks;
  • Neo provides tools for calculating liquidity ratios, detects frauds and deals with margin calls; and
  • SmartWealth, which delivers to its wealthiest clients fully personalized investment strategies.
One of the most significant investment companies in the world, Goldman Sachs Inc., uses AI to automate the way a bank deals with initial public offerings (IPO). Its AI tools are already in charge of tracking reviews, filling forms, and generating reports.
But, IPO is merely one of the thousands of possible applications of AI in the wealth management field. Xavier Menguy, a partner in Goldman Sachs, recently revealed just how much Goldman Sachs is interested in ML and AI:
“ML models are being used across wide areas – from business intelligence and flow analysis to inventory management and derivative pricing, among other things. With that in mind, we're investing heavily in ML and this is directly reflected in the backgrounds of the people we're hiring.”
AI at the Institutional Level
At this point, it’s important not to forget that asset owners vary and that, consequently, investment strategies differ. Even at its broadest level, there are two types of asset owners: individuals and institutions (sovereign wealth funds (SWF), pension funds, insurance funds, etc.).
Saudi Arabia recently became the first country in history which has granted citizenship to a robot. So, it’s not surprising that it is at the forefront of developing wealth management AI tools.
Case in point, recently it was announced that it has invested in a robotics initiative through its SWFs. Together with SoftBank Group, a Japanese technology company, they aim to incorporate AI in both the private and the public sector.
Speaking of Japan – its government pension investment fund (GPIF) partnered with Sony CSL to be able to apply AI tools in asset management.
Hiro Mizuno, the chief investment officer and executive managing director at GIPF, commented on the occasion:
“Given recent developments in AI technologies, GPIF is increasingly convinced that AI would be able to assist our fund investment operation, from asset allocation to asset manager selection and evaluation processes. By partnering with Sony CSL and leveraging their cutting-edge AI research, GPIF aims to stay ahead of the curve to remain capable of fulfilling our fiduciary duty to future generations.”
All in all, although individual investors - even if they are global investment companies (like Goldman Sachs) – are more likely to try emerging tech tools, it seems that even institutional asset owners have already embarked on this road too.
Since recently, their investment strategies appear to have shifted towards implementing modern technology, first by financing the development of reliable AI tools, and then applying them at all levels.
Conclusion
At the end of 2017, Sarbjit Nahal, head of the investment strategy at BAML, said:
“Asset owners across the world, be it petro-funds or SWFs, are becoming increasingly systematic in terms of the way they take these trends on board.”
Investment firms and institutional funds of all types and volumes are using, considering and/or investing in AI tools. This is more than promising. Expect faster development of AI technologies asset owners and managers will trust.
And get on board as soon as possible!
If you use the face unlock feature of your smartphone, you’re already using facial recognition technology. Companies like Google are already using facial recognition technology to group all your photographs together.
Simple as it sounds, there are many complex activities going on in the background that make facial recognition possible. We break it down into four steps for the sake of simplicity and understanding:
  1. Capture: Your picture is taken from a video or a photograph. Whether you feature alone in it or are walking in a crowd, high definition CCTV cameras are smart enough to determine different faces in a scene and capture them separately.
  2. Facial Analysis: This is where things get really technical. Different features of your face – the distance between your eyes, shape of cheekbones, dimensions of forehead, distance between forehead and chin, distinguishing facial landmarks, etc. are all analyzed and stored. There are up to 80 nodal points in a human face, that can be combined to identify it uniquely.
  3. Face Printing: All the analysis points are converted into mathematical formula by assigning them numbers. The resultant data is unique to a particular face and is called facial signature or a face print. This can be thought of as a digital signature or a biometric of your face. This is stored in a database. American police have about 117 million such faces already stored in their databases, which comes handy during investigations.
  4. Matching: Now that the faces have been codified and millions of face prints are stored in inter-connected databases, it becomes easy to match a new face against these. The subject’s face has to be digitized, its face print generated and matched against millions of records sitting in the databases. The FBI has ready access to over 641 million digitized faces!
Given the advancements in mobile devices and high-speed wireless connectivity, this entire process is often completed within seconds. As technology is becoming more reliable and affordable, facial recognition is getting plenty of traction across industries – more on that later.
What’s the catch with Facial Recognition Technology?
The biggest concern with facial recognition is that your facial data is often being captured without your permission. Our cities are full of Close-Circuit Television (CCTV) cameras – parks, shopping malls, highway toll plazas, airports, residential societies, streets – they are everywhere! As we now know, all it takes is a single image or a video footage to extract facial data, process it and store it in the form of a unique faceprint. It can be used for malicious reasons such as gaining unauthorized access to systems, wrongfully authorizing financial transactions and much more.
Sometimes, you yourself share your facial signature without realizing it. Think of the countless selfies and other photographs you’ve uploaded on social media websites – are you really sure their use is restricted to the intended purpose? Facebook has already been ordered by German and Irish data regulators to delete all the facial recognition user data it had gathered for suggesting tags, as users were not giving their consent.
How can Facial Recognition Technology (FRT) and GDPR go together?
GDPR defines biometric data as:
[Biometric data] means personal data resulting from specific technical processing relating to the physical, physiological or behavioural characteristics of a natural person, which allow or confirm the unique identification of that natural person, such as facial images or dactyloscopic data.
Facial data clearly falls under this.
Given the manifold benefits of FRT, it would be unwise to ignore it because of privacy concerns. Instead, you can have the best of both worlds – use cutting edge Facial Recognition Technology while being on the right side of the law. Despite its limitations, GDPR has provisioned clauses under which FRT (and any other technology that uses biometrics) can be used after taking user consent.
These are the use cases where FRT can be applied fairly easily:
  • Employment or social security related verification
  • Protect an individual’s interests at a time when he/she is incapable of giving consent
  • Covering legal issues
  • Public health emergency
  • Include FRT specific Data Protection Impact Assessment (DPIA) policy
  • Anonymize/pseudonymize the data so it becomes impossible to associate with a person for outsiders
We will explore FRT’s legal and implementation aspects further in the next articles of this series.
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