Money makes the world go round, or so they say. Payments, investments, insurance and billions of transactions are the beating heart of a fractal economy, which echoes the messy complexity of natural systems, such as the growth of living organisms and the bouncing of atoms.
Financial systems are larger than the sum of their parts. The underlying rules that govern them might seem simple, but what surfaces is dynamic, chaotic and somehow self-organizing. And the blood that flows through this fractal heartbeat is data.
Today, 2.5 exabytes of data are being produced daily. That number is expected to grow to 44 zettabytes a day by 2020 (Source: GigaOm). This data, along with interconnectivity, correlation, predictive analytics and machine learning, provides the foundation for our AI-powered future.
More than $2.1 billion has been invested in AI-infrastructure startups since 2010, with $1.3 billion being invested in 2015 alone. AI-application startups have seen the largest share of the investments, with more than $6.9 billion being invested in AI-applications startups since 2010 and a total raise of $3.6 billion in 2015 (Source: TRACXN).
These movements are leading toward massive innovation being delivered in the financial services field, and AI is helping address the tensions of increasing data volumes, changing demographics and their wants, regulatory tensions, organizational and systems efficiency and a changing technical landscape.
We now see more than 500 million people using bots and digital assistants. That is predicted to rise to over 2.2 billion by 2020 (Source: Statista). Those platforms, developers and data science teams that train AI are aiming to create a friction-free and simple experience with the devices we use to reduce the need for human-to-human contact to increase interactions. This is especially true for the banking industry to solve, where younger customers would rather see a dentist than listen to what their bank is saying (Source: Millennial Disruption Index). This means disconnection and ultimately defection to other app-based platforms.
Right now we do have a real issue with bots. When adoption of a system is low, the experience offered is not an optimal one. Even banks such as Royal Bank of Scotland in the U.K., which is launching a bot called Luvo in its service channels, is aware of this: Although Luvo initially needs to be trained to understand subjects, RBS insists it will earn its AI stripes by “learning from its mistakes,” which will make it “more accurate over time.” In the meantime, however, customers have to have patience while confined to a sub-optimal experience.
SEB in Sweden is also deploying a bot, called Amelia (by IPsoft), for service to their 1 million customers. In addition, they have put it to use internally by deploying it to provide tech support for their 15,000 employees. This has led to a solid implementation.
Beyond this, and closer to the customer, is the rise of “conversational commerce,” which is a mobile system that uses AI to parse speech and undertake anticipatory actions such as ordering your Mom’s favorite flowers for her birthday, or paying back your friend for money borrowed on a night out. Samsung stepped up and bought VIV, and it’s rumored that the next iteration of Apple’s Siri will also evolve into the conversational commerce space as the payments ecosystem develops to make it easier for us all to be liberated from our hard-earned income toward networks of retailers.
Beyond the bots, we will also look to robo-advisors for helping us with our investment portfolios and to deliver better returns. Companies like Wealthfront and INVSTR are stepping up to the plate in North America and the U.K. There have been some impressive results in Korea, as well. Some robo-advisors are delivering 2 percent returns versus domestic equity funds at -3 percent and KOSPI at -2.2 percent.
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