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. 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 at the statistics in 2023, already 486 billion fraud losses worldwide, 800 billion money laundered worldwide. 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 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 we embarked on a transformation journey, and we envision a world where people win and crime doesn't. 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 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.
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 called it's about balanced leadership, people and performance. Dare and care. Dare to give space to the engineers. They make it a human centered transformation. They're very kids, 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.
So they're all working agile at scale, same rhythm, and it's connected by communities. Women in tech community, Java community, but also bigger 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 bigger days, of course. And we also organize a lot of hackathons and innovation days. 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 JNI?
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 dare 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 structures. Unstructured. Very important to make it work well.
And the last one is care for responsible AI. 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? Ensure your balance. Your balance of bearing care. If you start working with AI so there to invest, there to transform, there to give space, but also there to start small but scale fast and care for high quality data, care for responsible AI, and care for a thriving engineering community.
