Marc Hauser is a founder, investor, and innovation enabler with over 15 years of experience in banking and fintech. He is currently the Head of Europe at Tenity, where he helps startups scale their businesses and connect them with leading financial institutions.
If you work at a large corporate and are interested in the topics of artificial intelligence and banking, you may be in one of the following situations:
In the past decade or so, AI has had a limited number of use cases due to the huge amount of computing power it required. But with recent breakthroughs in deep learning and the subsequent release of ChatGPT, generative AI is taking the world by storm.
The way I interact with website chatbots is a clear example of how AI has evolved over time. 5 years ago, when a website prompted me to interact with a chatbot I would nearly always click on “talk to a human agent”.
Today, AI has improved chatbots so much that using a chatbot is usually more efficient to get something done. Now, I nearly always prefer using a chatbot when prompted.
I believe that in the future, AI will work as a co-pilot for every person working in pretty much any job including banks, and we’ll be using tools like chatbots on a day to day basis.
Based on my experience working at UBS on wealth management, co-founding two tech companies and my current position as Head of Europe at Tenity, here are my thoughts on how AI will transform banking:
Note: do you work for a corporate and are trying to understand how to implement AI in your company? Reach out to us to see how we could help you.
When a new customer creates an account at a bank or financial institution, AI is already used by auto-completing and auto-suggesting fields. But I believe it could do so much more.
In the future, AI will act as a customer onboarding guide, offering new users an Uber-like experience. This guide will know the customer so well that it’ll help the user decide which type of bank account would suit them best, and then set it up with them.
Since a lot of the loan application process is very manual, I see AI automating a lot of the process and reducing approval times for facilities like loan disbursement. The AI guide will also play a large part in helping the customer apply for a personal loan or credit card.
A great example of a bank already putting this into practice is Capital One. Capital One launched Eno in 2017, a chatbot described as an intelligent virtual assistant. Once you create an account with Capital One, you can use Eno to track your spending and manage bank admin tasks like checking their balance and adjusting a recurring charge. Eno will also alert you when a suspicious or new charge comes up.
I believe we’ll see a lot more AI virtual assistants like Eno in banking which will help customers with onboarding and specific admin tasks. For banks, this will help decrease costs, better assess credit risk and drastically improve the onboarding experience.
According to a Global Banking Fraud Survey by KPMG, payment fraud has been on the rise since 2016. It’s likely this number will increase even more as bad actors use these AI tools to scale their fraudulent activities.
In order to fight AI fraud, banks will need to use AI tools and to have the right cybersecurity in place. For example, before it was easy to detect a phishing scam because it would usually be full of grammar and spelling mistakes. But with Large Language Models (LLMs), fraudsters are now able to write much more polished copy and make it seem a lot more realistic. Fraudsters are also using AI to create more realistic email addresses and landing page that look a lot more like a banking page.
In order to fight this type of fraud, banks will need to improve their Know Your Customer (KYC) and Anti-Money Laundering (AML) processes. They also need to invest in educating and warning clients, for example by sending an alert before a customer sends money. They’ll also need to start using an AI tool that would allow them to detect scans via passive signals that are harder to generate with AI, such as hesitation time, copy pasting and IP addresses.
An example of a bank using AI models to detect fraud is UBS back in 2019 (which demonstrates that AI for fraud management isn’t anything new). The UBS Card Center’s fraud team used AI technologies within the FICO Falcon Platform to stop 84% more fraudulent transactions than in 2015. They also managed to investigate and resolve 42% more fraud alerts, while not bringing any new resources.
Millions of people already use tech powered wealth services like robo-advisors and index funds. But these are still quite limited: most robo advisors will only offer 6 key strategies for a customer to pick, limiting their choice.
What if retail customers could access the same level of personalised banking services as a customer who has hundreds of millions? I believe that’s possible with an AI powered wealth advisor.
With an AI advisor, the tool can go through all of the customer’s historical records, spending patterns and bank accounts, and create a unique strategy based on their personal information.
In fact, it’s possible we’ll reach a stage where people will prefer to have their money handled by a robo-advisor instead of a human advisor. Think about it: if you asked a human and a computer to do the equation “24 x 56”, who would you trust more to have the right answer? Probably the computer.
We already have aeroplanes that are mostly driven by computers, so it’s not too much of a leap to believe that wealth management will be AI driven, and possibly preferred, by customers.
Companies like Morgan Stanley are already trying to implement this. They recently announced that they’re creating an assistant that is created via Open AI’s latest software. The tool acts as support for financial advisors, giving them quick access to a database of 100,000 reports and documents. As Morgan Stanley’s co-President says in an interview with CNBC:
“By saving advisors and customer service employees time when it comes to questions about markets, recommendations and internal processes, the assistant frees them to engage more with clients, he said.”
I believe we’ll start off by seeing AI tools support financial advisors first, and then possibly replacing them for specific tasks.
There’s a lot of exciting possibilities when it comes to exploring the applications of AI and the banking sector, but it won’t come without its challenges. I see three main ones:
In order for AI to work well, the model needs to process a lot of data. Banks do hold a lot of data, but it’s often scattered across many different systems and datasets. And because of banking legacy systems, it can be very hard to pool it altogether. Without the right customer data, AI tool development will be a lot slower.
This is also highly linked to privacy concerns. Would you be comfortable sharing your banking data and credit scores with the bank’s AI? People will want to know how AI is using their bank data, which means banks need to have data governance, stewardship and the right rules in place.
Customers will want to have the freedom to choose how their data can and cannot be used, and banks will need the right systems in place to remain compliant.
Traditional banks usually don’t have many data scientists and AI experts in-house (which isn’t so much the case with fintech challengers). But in order to make the most of AI, banks need to prioritise having the right people as part of the team.
This goes hand in hand with the right culture. I’ve experienced culture clashes first hand when working with financial institutions: if someone with influence doesn’t trust or believe in AI, then it’s a lot harder to get a project going.
Peter Diamandis, Co-Founder and Executive Chairman of Singularity University, says it succinctly:
“There are going to be two kinds of companies at the end of this decade: Companies that are fully utilising AI and companies that are out of business. And it's gonna be that black or white.”
Will we be able to translate the trust people have for Swiss bankers into a digital environment? I believe we can, but it’ll take a lot of work in the world of digital transformation and automation. The onboarding journey has to be slick and customer centric. The tool has to be exceptionally good. It has to be easy to get the right support.
Banks need to offer all this while still remaining compliant. I believe maintaining that balance between customer centricity and being compliant will be one of the banks’ biggest challenges.
As the Head of Europe at Tenity, I get to see new initiatives at the intersection of fintech and banking. Since the use of AI is a hot topic currently, a lot of startups are focusing a lot on implementing use cases in this sector.
One great example is Enterprise Bot, a chat tool or software that uses machine learning to power conversational chatbots, email and voice bots. It has its own proprietary algorithm (DocBrain) and is also integrated with ChatGPT. The company has been around since 2018, and graduated from a Tenity incubation programme batch at that time.
EnterpriseBot helps banks manage customer service queries, while also offering customer support in 5 languages. EnterpriseBot has raised CHF 2.5 million so far, and has a global team of 50 full-time employees across Europe. They work with some of the biggest customers including Generali, SIX, LNER, Swisscom, Cognizant and many more.
By being part of our innovation ecosystem, they’ve been able to work with other corporates and startups working on AI and digital banking. In 2018, Enterprise partnered with Apiax, a solution that helps digitise regulations and manage digital regulatory compliance rules. They’re working together to help build a personal digital compliance officer, which could help bank employees get answers to complex regulatory questions.
Companies like Enterprise and Apiax are pushing the boundaries with AI, and since they are startups they can work 100% on the right use case. With our Tenity ecosystem, they can then partner and collaborate with banks and other financial institutions.
At Tenity, we operate as an innovation ecosystem where we support corporates and startups via workshops, collaboration and investments. We run our own startup incubators twice per year, we run accelerators for corporations and also have our own fintech fund for investments.
If you’ve read the example above, then you can see how two entities can find ways to work with each other by being part of our ecosystem. When it comes specifically to AI, there are a few ways we’re enabling more AI use cases in banking.
Luigi Vignola, Head Markets at Julius Baer, one of our partners, describes it best:
“It’s a bit of a laboratory for us. We can throw in questions and see if somebody can come up with a smart solution without using too much of our own resources, which are largely committed to the day-to-day processes.”
At Tenity we operate as an open innovation ecosystem, which means companies innovate by opening up their strategy to third parties. Our ecosystem includes:
Since we’re constantly working with startups and corporates on the latest technology, we can stay on top of trends in the fintech and financial services space a lot more easily. This in turn means that when a startup or a bank joins us, we can match companies and enable collaboration.
You can see many of the collaborations we enabled here: Corporate Success Stories
Tenity was born from corporate innovation. Our CEO, Andreas Iten, led corporate innovation for SIX, the Swiss Stock Exchange. He eventually set up Tenity as a spin-off from SIX, and we’re now independent.
We know first-hand what corporate innovation looks like. We’ve come from it ourselves, we’ve seen the challenges and know how to overcome them first hand.
We’re also specific to financial services and fintech. Other innovation ecosystems will cover a wide variety of industries and also do fintech. We only do financial services, which increases the likelihood for a bank that partners with us to find a startup with a use case that makes sense.
You can read about how Julius Baer worked with vestr, for example, via Tenity, allowing them to create a solution to improve the handling of Actively Managed Certificates (AMCs): How Julius Baer and vestr successfully built a platform to digitise AMCs
Being fintech specific and having a strong background in corporate innovation means we can easily stay on top of trends in AI and banking and can help match startups to the right partners.
Innovation is more than just one corporate accelerator program or one workshop: it never stops. When a corporate partner works with us, we help guide them no matter what stage they are at in the innovation cycle. We see corporate innovation through three lenses:
We help corporates understand and learn about the world of startups with:
1. Community events.
3. Executive events.
4. Startup database.
We help corporates and startups find the right partners to start working together:
1. Deal flow sessions.
2. Collaboration support in proof of concepts (POCs).
We help corporates become part of the startup world via investment or acquisition. At Tenity, we enable this with:
1. Join our investor community and become part of the ecosystem.
2. Become a Limited Partner in our fund by investing in our early stage fintech fund
When we partner with a corporate, we define a thesis and establish goals for using AI. Our three pillar approach means that no matter what stage the corporate is at, we can adapt.
Based on my work at previous companies and what I see at Tenity, I strongly believe AI solutions could help democratise banking and take the industry to the next level. It’ll help banks offer a much better customer experience, fight all types of fraud and offer customers a more personalised wealth service. I believe we’ll only see more of this in the next few years.
If you work at a corporate and are looking to partner with AI startups, reach out to us to see how we could help you.