The Evolution of AI: Can Investment Morality be Programmed?

Back from China – InvestGlass was invited by Swissnex and the Swiss Embassy to present its artificial intelligence developments in China. An amazing roadshow organized by Felix Moesner, Swiss Consul & Swissnex China CEO and his team. A three days roadshow, in three cities, Shang Hai, Hong Kong, and Beijing. Each night Swissnex organized a round table confronting Swiss and Chinese AI specialists. 

Media Credit – Felix Moesner swissnex China CEO | Consul swissnexChina.org

Early fear around robot advisor. 

The early fear around robot advisors was that technology would replace what bankers do. Certainly, we now know that a robot can filter information faster than a human does. For a set problem, a robot will react quicker and smarter than a human…yet in reality, robots have failed to capture even 1% market share of investable assets! Perhaps because what we called robots were often marketing window dressing. Actual retail robot advisor clients are “self-directed”, tech-savvy, 30-50 YO clients – not exactly anyone.

Disruption is visible in both “advisory” and “discretionary portfolio mandate”. The game is to speed up the customization of portfolio rebalancing. What we have noticed in Europe: is the bloom of institutional robots. A new generation of institutional managers building model portfolios which you connect to investment management platforms like InvestGlass. A new generation of independent financial advisors outsourcing investment decision process: Made in Roma, Designed in Zurich. We are leaving the question of quality control aside for now.

Speed is crucial. In Switzerland, account opening remains a compliance department dilemma, frustrating digital clients – particularly foreign investors. However facial recognition techniques and do-it-yourself questionnaires can increase the pace of account opening and compliance checks. Reducing from a few weeks… to a few days… In China, WeChat, the equivalent to our WhatsApp chatting application gathers so much information that the credit scoring and lending process are as low as 30 minutes! Data and A.I. explains part of it but another reason is slow API adoption in Switzerland as well as traditional IT vendors’ reluctance to open gates to third-party API.

As our Chinese colleagues, we believe that robotization is the first step to speed up manual, repetitive and low-added-value tasks. Reducing a dozen staff members to just one with a good bundle InvestGlass + API + Fintech partner vendors. Investors want to be served as they would be in a Starbucks. The modularity of data, applications and AI is mandatory to offer a mass-customized experience. They want their order to feel “unique” – respecting a daily ritual where the client is king. Fintech SAAS and now BAAS are the future!

Large Americano, Soy Milk, Low fat.

Since the new European regulation, MIFID2, financial products’ risks are scrutinised and should be compared against clients’ key information (KYC and more). Would a Starbucks vendor ask you if you are caffeine, milk or aspartame intolerant? Would a doctor check your allergic risk, cure success rate and DNA compatibility before he prescribes you a medicine? Well, we expect bankers to do so. Without artificial intelligence, this process is just impossible! We found that China’s financial regulator does not require this level of risk control and price transparency…yet. However, he is putting pressure on fixed interest products vendors 固定投资 – guaranteeing extremely attractive yields… robots and managed portfolios can help.

In Europe, ingredients are standard and it offers more flexibility for robotic model rebalancing. With one button a trader can rebalance 5’000 clients at once and check 5’000 KYC at once. Reducing a whole day of work to one or two hours thanks to a banking AI. It does not mean that people are going to lose their jobs. It means that administrative tasks will be more efficient. The job description will change – advisors will focus on high-value tasks. Call, empathy, storytelling, building communities etc…

And what about robots? Can a robot feel empathy? Or entertain a human community? Can we be rational without emotions? We don’t think so yet. Emotions are dynamic valuations with recognizable similarities or facts. Machines can show some empathy if they mimic our goals.

We, humans, are granting “automorphism” to machines. Digital banking is now a reality.

This is the missing link for robots to predict what we need to feel good. Naturally, we are granting some herd empathy to robots. Advisors’ fears should no anymore be about a compliance KYC issue, cash wire, and portfolio rebalance… It should be about what role they wish to play in this new robotic/human flock… a cyborg environment.

Robots are programmed. 

At each Swissnex evening in China, the audience asked the same question: “Is it possible that one-day robots will win over humans?” 

My answer was clear: “Yes they already do in many aspects”. Robots are programmed to win over humans at least when we humans understand the process to optimize… for the rest it is far from reality just because we still don’t understand our whole brain, independent organs etc… Furthermore, robots will be programmed with herd and empathy components to reflect and generate feelings. The field of “affective computing” is currently where InvestGlass focuses its research efforts.

The good news is that artificial intelligence could be empathic. Empathic means understanding someone’s emotions based on someone’s referential. 

Moral values are also computed inside the machine. 

Affective computing is used to detect emotions, record emotions into dialogue, and then generate sentences inside investment illustrations. Mimicking emotions is possible and it increases the credibility of an investment solicitation. At InvestGlass, emotional optimization is an important topic of research and development. Understanding moral values are not something self-evident. 

Ethical decisions are based on our culture. Clearly, information is not data. This was the main controversy in China. Chinese speakers believed that solving the problem relied on data quantity, whereas Swiss guests, possibly considering Cambridge Analytica and GDPR, thought that an agile algorithm and some reinforcement learning might suffice. Indeed, context awareness is key. If we say it’s 25°, this data is not sufficient. We need to know if this is °C or °F. 25° C is the real data. But when we say it’s 25°C the temperature IS NICE. Saying that it is pleasant means that we place ourselves in a context where 25°C is normal. Humid summer in ShangHai versus dry summer in Geneva?

At InvestGlass, we are focusing our AI algorithm on supervised and reinforcement learning – leaving unsupervised to more on-demand customizations such as trading patterns recognition, and call report patterns recognition to improve clients segmentation.

Even with small data versus the “big Chinese data fair”, we achieve improve financial advisors’ efficiency by understanding moral values in investing, giving, lending and saving. Certainly, math and data play a role, but randomness, moral habits, social norms, and financial regulations also influence the ignition of an optimal decision.

It is possible to program and individualize investment morality.