PegaWorld 2025 Rabobank keynote: Powered by People: How Rabobank’s Culture Fosters AI Innovation

AI, I think, took us all and the whole technology and frankly, the whole world in some pretty exciting new directions. And clients like Rabobank, who I have had the pleasure of working with over the last couple of years, are really leading the charge in figuring out how to take innovative technology and actually drive better experiences for their clients, their employees, and overall their impact on the world as a whole. So let's take a look at some of the cool stuff they're doing. Rabobank is a cooperative bank. A bank without shareholders, but with over 2.3 million members. We strive to help people and businesses achieve their goals and provide solutions for the challenges of our time that cannot be solved alone. We want to be a stable factor in a volatile world. With our global network, our knowledge and financial solutions we aim to make a positive impact on our food system, our energy system and financially healthy communities. Our cooperative mindset is reflected in our products and services, and in the way we give part of our profits to our members, customers, local communities and society. We believe in the power of collaboration to strengthen communities and drive the shift towards more sustainable economies and societies. That's why our mission is "Growing a better world together". All right. Thank you. Yeah. So without further ado. Hold on. I don't very often get the chance to do this, so drum roll, please

Please welcome Rabobank's Global Head of Financial Economic Crime Tech, Erica van de Ven. Well, thank you, Don, for this energizing introduction. And good morning, all here in the arena. And good morning and good afternoon, all of you watching online. And a special good afternoon for all the Rabobank teams watching in the Netherlands, or maybe even in India. I imagine you had a great day yesterday. It was very inspiring. And you must be interested or people already working with Pega on improving your customer experience or your workflow automation. But you must be dealing with a lot of change as well, technology wise, geopolitically, or in having a lot of regulatory requirements. And so am I and so is Rabobank. My name is Erica van der Ven. I'm responsible for the financial economic crime technology, proudly working for more than 20 years for Rabobank now and mostly in technology, actually in payments in the heart of the bank. And since three years, I am global head of tech. And if I look at Rabobank, you've seen the video. I'm really proud to work for Rabobank. It's a cooperative bank caring for its customers, caring for society. And it exists of three strategic business lines. One is the retail bank in the Netherlands.

The second one is a global food and agri bank, and we want to be the food and agri bank of choice. And the third one is a global vendor finance company, De Lage Landen, DLL. And today I would like to take you through our transformation journey, actually, powered by people to enhance our customer experience and our employee experience and in collaboration with our strategic partners. And most of them are here actually. And with a strategic partner like Pega. As I said, I'm working for the FEC domain within the bank. And our purpose is to safeguard our customers, the bank and society by detecting and preventing financial, economic crime. And you can imagine that's not so easy, because if you look at the statistics in 2023, already 486 billion fraud losses worldwide, 800 billion money laundered worldwide. The Netherlands is a small country, actually, but we're on the secrecy index on number 12, making us attractive for financial economic crime. And you might wonder what is financial economic crime if you're not working in the financial sector. You have to think about money laundering, terrorism financing, sanctions circumvention and fraud. And a couple of years ago, we still had standalone systems, standalone technology across the bank, built up in several departments, and we had to do a lot of manual work to make detection and prevention happen. So much manual work that actually we ended up with 20% of the bank working in FEC. And you can imagine that wasn't sustainable at all. So we embarked on a transformation journey, and we envisioned a world where people win and crime doesn't. So we defined a strategy. And in that strategy, the fact strategie, we defined two topics which define the technology.

We said we wanted to achieve a baseline by end of 2024. And a baseline was defined on all kinds of model enhancements, workflow enhancements and data enhancements. And we defined that we wanted to innovate to lead. And I think that typifies us. We were still aiming for the baseline, but we also wanted to innovate to lead. And if I then take you on our transformation journey, as I said, there were all kinds of standalone systems. And what we did in the last two and a half years related to the models, we made them a new technology, actually, and we extended the coverage enormously. So, for example, in fraud detection, you could already notice that in 2022, we still had millions of fraud losses every year. And now it's in the thousands of fraud losses. The same holds for case management. Case management was standalone and analysts had to copy and paste data in. They had to search for data. And it took ages, actually. And now for the critical processes, for AML and for customer due diligence, we have guided workflows built in Pega with routing, prioritization, they have dashboards in which they can see the data in the right spot, in the right moment. And the same holds for our data. Instead of looking it up in all kinds of source systems, we now have a global data platform with strict data governance. High quality data. So we came a long way in two and a half years, but we still have a lot of ambition. We would like to enhance the models with machine learning, and some of them already are, actually, making them more effective than the benchmark in Europe. And we want to fully enhance our workflows with GenAI.

And I would like to take you on that journey as well. And if you look at the highlights, it started last year with an idea of Mart Gombert and Himanshu Upadhyaya. And they are here, actually, they will be telling more about GenAI in the breakouts so make sure that you're there. And they started with a guide for the analysts, actually a Buddy, having all knowledge for the analysts together in that Buddy. And maybe even you could put your tickets or your problems in that Buddy. And it was an idea in 2024 but we brought it already live in 2024, so hundreds of analysts could already work with it. And we scaled up to 3000 now. And we aim to do full coverage this year. The second topic on GenAI is how to get from speech to text, because analysts call our customers. For example, during onboarding. And you can imagine if you have a call of one hour asking everything about how the customer works and you then have to summarize, it takes a lot of time. So we wanted to have the speech summarized for the analysts, and luckily our retail colleagues already were working with the system who could do this. So they helped us and we connect it to the flow in Pega. And now in 2025, we're working on all kinds of summarization cases. There's a lot of summarization going on in the workflow. You can imagine that an analyst has to summarize previous cases of the clients, or has to summarize all the onboarding documents of a corporate client. And that's a lot of work. So we're now working together with Pega on making these GenAI use cases work.

And the last one is we imagine that we want to generate a report, actually. So we keep the analysts in the loop. But imagine that we can make the ultimate report in the flow, even with a draft conclusion made by GenAI. And then the analyst can do what he is good at look, at the risk, do the analysis and make the ultimate decision. And apart from the workflows, we're also looking into other types of GenAI use cases already. You can imagine we have a special investigations team who's looking more deeply into crime. Can we help them to uncover emerging risks in the market? And even when I was preparing for this keynote, we were doing experiments with agentic AI. Can we make a client view with agentic AI? So a lot coming up still. And if you then look at our factors for success, the first one, and I'm really proud of that, is our people. We really dare to give space to our people. We have a leadership program running in the bank and it's about balanced leadership, people and performance. Dare and care, we talk about. And if I apply it to this case, dare to give space to your people. Dare to give space to the engineers. They make it a human centered transformation. They're very young people, actually, both engineers and analysts. And they're very keen to do the AI adoption. We also have a culture of innovation and collaboration. The whole bank, all engineer teams, are actually working agile at scale. So we could do this "No sprint without a print". So they're all working agile at scale, same rhythm. And it's connected by communities. Women in tech community. Java community. But also Pega community. And it's a very active community, actually. They're organizing a lot of knowledge sharing forums.

Talk about innovation, how to take it further, proud of Pega days, of course. And we also organize a lot of hackathons and Innovation Days. Real world challenges. 24 hours. Four hours. Can you solve this challenge? For example, the one with the onboarding documents. Or can you solve that I need this data in that place of the workflow? So there are a lot of hackathons making the teams challenge. And you see two teams here who celebrate their victory, actually. And if I then continue on the dare and care, there's more factors for success. Our managing board last year asked me, my colleague in retail and my colleague in data to make an acceleration program. They asked us, can we accelerate on GenAI? Will it work in the bank? Can we make a business case for that? And we did, actually. And they dared. They dared to commit and they dared to invest. And we report back every quarter how we're doing. And then on the care side, make sure you care for high quality data. Structured. Unstructured. Very important to make GenAI work well. And the last one is care for responsible AI. And maybe Rob will tell more about it. And we invest already to make sure that we can care for responsible AI. We make responsible AI building blocks, we call them. So all teams can use it to test and monitor the risks of AI. So is there no drifting or no bias? There's a dedicated AI officer in my team, and she takes care of how teams are developing their machine learning or GenAI use case. And all teams work according to an AI way of working. So they go through stage-gates and sometimes they don't like it because they want to go even faster. But it makes that we are more assured that it's responsible what we are doing. So to wrap up. Ensure your balance. Your balance of dare and care if you start working with GenAI. So dare to invest, dare to transform, dare to give space, but also dare to start small but scale fast and care for high quality data, care for responsible AI, and care for a thriving engineering community. And with that, I would like to wrap up. Make sure that you join us if you want to know more. Himanshu and Mart can tell much more about the GenAI use cases. We are 11:30 just after the keynotes. Premier ballroom 315 and we hope to see you there. You can ask us all about our journey and about the journey of our use cases. Thank you very much!