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AI-driven algorithms are now actively being put to work now, for example, in equity trading and some areas of investment management. AI’s advantage, argues one trader interviewed for an Economist report, is about the accuracy of investment decisions rather than about their speed. Deliberate but more accurate decisions should ultimately mean better returns and reduced risk.
AI Business caught up with the Chief Digital Officer at BNP Paribas Wealth Management, Thierry Derungs. With over 25 years of experience, today his job involves overseeing the Digital Strategy for the company – providing country-based support for the deployment of all new projects. Derungs claims his title should really translate to chief disruption officer. Today he manages a wide set of ambitious digital programs, providing new solutions to support and implement their digital strategy around the world. Derungs believes that artificial intelligence is the hottest topic today for the financial sector, particularly with Big Data, RPA, and everything connected to voice.
Derungs believes that although wealth management has primarily focused on robot-advisory in recent years, the breadth of AI applications is far greater. He believes that the industry needs to face up to the technological wave for widespread implementation.
“AI has existed for more than 50 years but only now has it become a reality for all of us. Indeed, only very recently has AI jumped from high-tech research labs to into our pockets (Siri)”. This accessibility means that many need to catch up and work harder. "The sector faces expectations to deliver on ease and simplicity. There is also a significant increase in complexity with regulators (especially when it comes to cross-border) and increased pressure on costs and operational efficiency.”
Derungs argues it is fundamental we confront some of the major misconceptions of AI today for emerging technology to be harnessed effectively. “Although impressive, AI cannot work miracles. It is not a magical box that can solve everything –something you can apply from one day to another. For AI to work for you it is vital that the enterprise establishes the strategic mind-set before implementing.” The entire business must get ready for change with strategy led from the top of the organisation.
As with other industries, there is a strong fear that AI will wipe out jobs overnight: “Of course, the world is changing and the financial sector is experiencing significant disruption. However, for us, we believe the impact will be on the business model. In other words, Jobs will not disappear but the nature of the jobs will change." Derungs strongly believe in the idea of the next generation Relationship Managers, augmented by new technologies and in our experience this is what the client wants.”
“When it comes to new technologies, AI provides the greatest range of opportunities. We can see the capabilities at many levels inside a financial company from client services, risk management, operational efficiency, security & fraud, IT support”, Derungs argues. “We are in the middle of a tectonic business shift which will change the way financial services are perceived, provided and managed. This is not only about new business models but also the way they will be delivered to clients. For example, I believe that NLP (Natural Language Processing) is the future of client interaction. Why go to an App? Why type text into a chat? When you could just talk.”
“But I must underline that AI on its own has no value… what will lead to the quantic business jump is the combination of AI and digital capacities, big data, RPA, biometrics… Exactly the same when you are working with API. What provides the highest value to move to API is the new capacity to combine them into new approaches and services.”
“As I previously discussed the AI field of opportunities is wide. There is a high chance of loss, especially if it is looked within a technology prism. My initial advice for the investor is to be use case driven. In each use case try, learn, fail, adapt and… try again. AI could be such a Terra Incognita that its discovery must be driven by practice before any industrialisation consideration."
My second advice is tidily related to the first one. Choose a step-by-step approach where agility will not be key but just compulsory. Learning how to take the most of AI for a use case request a strong capacity to adapt and adjust.
Last but certainly not least, data is more king than ever. Even if "I" is for Intelligence, you should see AI as… stupid… Garbage in, garbage out, as simple as that. You can work on the most innovative and clever deep learning, its quality is driven by the quality of your data. When starting on an AI use case, be sure that you fully master the requested data knowledge and quality. This must go along an accurate feedback loop to improve not only directly your AI but also the data used.
“The Financial sector is much like others is highly regulated. We must be able to explain every decision taken in light of the client’s situation and knowledge, risk and appetite. These decisions are taken according to local laws and regulations, extended with all additional constraints when delivering cross-border business. This encompasses a lot. Explaining years after a decision driven by an algorithm that you built can already be a challenge. When you have a neural system, the challenge is greater but remains manageable. As soon as you are in deep learning, I should say that you could be very fast out-of-control."