Remy Overwater

Maarten: Good morning, Remy Overwater. Thank you for taking the time to speak with us ahead of the Leaders in Finance AI Event on June 5th. To begin, could you tell us a bit about yourself and the work you do?

Remy: Good morning, Maarten. Thank you for having me. My name is Remy Overwater, and I’ve been working at SAS for almost twelve years now. I started in our Professional Services department, where I was involved in implementing AI solutions for our customers. Over the years, I’ve held various roles within this department, progressing from project manager to Head of Professional Services for the Benelux region.
About a year ago, I transitioned to the Sales department, where I now lead the Financial Services Industry business unit for the Benelux region. Together with my team, I’m responsible for the growth of our business in the banking and insurance sectors in the Netherlands and Belgium. Our customers include leading banks and insurance companies. We focus on delivering solutions in risk management and compliance, fraud detection, customer intelligence — all powered by our AI and decisioning platform, SAS Viya.
Before joining SAS, I spent over a decade at a mid-sized consulting firm, where I focused on operational efficiency and delivering services that helped customers improve their business value.
At the heart of it all, I’m passionate about helping financial institutions transform AI from a buzzword into real business value. On a personal note, I live in Aalsmeer with my two sons. In the weekends, I’m quite active as a youth coordinator at the local football club — a role where my leadership skills come in handy as well. I also enjoy spending time on the water with my boat on the lake in Aalsmeer and going on active holidays, like hiking or skiing in the Alps.

Maarten: Thank you for that. It may be difficult to summarize, but if you had to, what do you enjoy most about your current role?

Remy: What I enjoy most in my current role is operating at the intersection of strategy, innovation, and real-world problem-solving. My background in consulting gave me a deep appreciation for the transformative power of technology, and in this role I can apply that every day. Whether I’m helping a bank modernize its fraud detection strategy or advising on AI governance, the objective is always the same: to create tangible value. That’s what I find most energizing and love in my job.

It’s both rewarding and enjoyable to work with my team and industry experts to craft the right strategies. There’s nothing more fulfilling than turning complex challenges into measurable outcomes and playing a role in making this happen.

Maarten: It’s interesting to hear there are so many opportunities in AI, particularly within financial services. However, I’d like to shift focus to the challenges. In your view, what are the biggest challenges currently facing the use of AI in financial services—especially at the top level?

Remy: Yes, I believe there are numerous challenges, but let me highlight a few. Generative AI is at the forefront of transforming global business, and the financial industry is beginning to tap into its potential. In banking, data lies at the heart of everything we do—and I believe this is a perspective widely shared across the industry. The opportunity to unlock even greater value from data is significant, particularly in driving efficiency and generating high–value insights. Many banks and insurance companies are already starting to realize these benefits. Many banking leaders are now asking key questions such as: how and where can generative AI be applied most effectively? And how can they ensure its full adoption and successful scaling across their organizations?
Our customers often share key challenges with us—I’d be happy to highlight a few.
First and foremost, data privacy and data security remain top concerns for leaders in the financial services industry – something we hear repeatedly. AI relies on vast amounts of data to generate reliable results, and banks must protect their customers’ sensitive personal information. Striking this balance can be a major challenge. One promising solution we advocate is the use of synthetic data. It mimics the characteristics of real data—which is crucial—and can help banks preserve privacy while also reducing the time, cost, and complexity involved in collecting and managing real data. Synthetic data can support fraud detection, improve credit decision-making, and help reduce bias, among other benefits.
A second major challenge is the need for transparency and explainability in AI models. Many financial institutions struggle to balance the capabilities of complex models with the need to make their decisions understandable—both to regulators and to customers.
Third, there’s the issue of human oversight and accountability. Who is responsible when an AI model makes a mistake—for example, in a credit decision? This question becomes even more complex with models like large language models, which can generate their own logic or rules, raising significant ethical, legal, and operational concerns.
These are just a few examples we are seeing across the industry. Navigating them won’t be easy. But with the right balance of innovation and responsibility, financial institutions can turn AI into a true competitive advantage.

Maarten: Thank you for those great examples. Could you share some trends in the AI space from the past year that have either concerned you, caught your attention, or perhaps even excited you?

Remy: Yes, of course. A lot is happening globally. As you’re aware, there are numerous trends in the AI space, and they are evolving at a rapid pace. I can highlight some examples that have caught my attention.
The first is the growing use of generative AI in customer service and compliance. We’re seeing a significant rise in the application of generative AI, not only for customer interactions but also to support compliance analysts. For example, AI models are now being used to analyze large amounts of transaction data to identify unusual patterns that could indicate fraudulent activity. This proactive approach helps financial institutions better manage risk and safeguard customer assets.
The second trend is the rise of s AI models that can create other AI models, commonly referred to as AutoML. This capability is accelerating innovation by streamlining the model development process. However, it also introduces critical challenges around the oversight and texplainability of these models. AutoML can create highly complex models that may be difficult to interpret, making it harder for financial institutions to explain model decisionsto regulators, stakeholders and customers.Ensuring transparency and accountability in this context is becoming increasingly important.
And then the question is how should financial institutions address this issue? Well I would recommend taking a step-by-step approach and integrating traditional machine learning models with newer solutions.
The last topic is the rise of AI-driven fraud and financial crime, which is something we discuss frequently with our customers. AI has the potential to both increase the threat of fraud and to serve as a tool for its prevention—working in both directions. AI-generated deepfakes and synthetic identities are some of the biggest fraud challenges faced by banks. However, AI-powered detection is also considered one of the most effective countermeasures. By using AI responsibly—reducing model bias, ensuring ethical data use, and explaining its decisions—financial institutions can enhance its effectiveness as a tool for fraud prevention. I think these trends highlight a significant shift. AI is not just evolving rapidly; it’s redefining the rules. Staying ahead of the curve means staying informed.

Maarten: You mentioned the rise in attacks by people committing fraud, impersonating others, and using AI to deceive financial institutions — all happening at an incredible speed, making it nearly impossible for bank employees to detect them in time. But you also said that AI can be part of the solution. Where do we stand now? Are financial institutions currently equipped to deal with these kinds of attacks, or where do you think this is heading?

Remy: Yes, I think they are. These companies are fully capable of protecting themselves against these kinds of attacks. In fact, we already see customers putting these solutions into practice — including with us.
But governance is just as important. How do you manage and control these systems? And, as we mentioned earlier, how do you explain them to regulators? The key concern is avoiding models that make decisions on their own — decisions that can’t be explained or reproduced.
For example, when monitoring or making a credit decision about someone, it’s essential that the outcome is based on clear, reliable rules. That does introduce risks, of course, but we see companies are really on it.
In highly regulated industries, many organizations already have robust governance measures to meet compliance requirements but have additional AI governance needs. SAS offers an AI governance solution for the banking industry, with SAS Model Risk Management as its foundation. SAS has also recently launched the AI Governance Map to help organizations with their AI governance. The tool maps the maturity level in the field of AI governance.

Maarten: So, you’ve mentioned a lot of examples so far. I was wondering, do you generally view AI as more of an opportunity or a risk for financial services?

Remy: Of course, there are also risks involved. As I’ve mentioned earlier, there are some challenges, but in my view, AI presents a significant opportunity for financial services institutions to become more efficient, customer-centric, and future-proof. However, this can only happen if they invest in responsible use, governance, and human oversight. There’s that point again.
Futhermore, I believe AI can help mitigate risks if implemented with the right models. Banks and insurance companies will be able to detect risks more quickly, prevent fraud, and enhance customer protection. So, I see AI more as an enabler for improvement rather than a risk.
Last but not least, trust is key. AI must build trust not only with customers and employees, but also with regulators. I’ve touched on some of these challenges earlier in the interview. I truly believe that those who view AI as a strategic enabler, rather than just a tool or passing trend, will be the ones leading the industry forward. That’s the vision we stand behind.

Maarten: That’s a great conclusion. Looking ahead to the upcoming Leaders in Finance AI Event, is there anything in particular that you’re looking forward to?

Remy: Yes, absolutely. While I’m interested in the entire event, I’m particularly eager to learn how other leaders in finance are approaching AI—where they see the most value, the challenges they’re facing, and how they’re adapting to the rapidly evolving expectations. I believe this is a fantastic opportunity to exchange insights, spark new ideas, and collaborate on driving responsible AI adoption across the industry. In such a fast-moving field as AI, collaboration is crucial for progress, and I’m looking forward to learning, challenging ideas, and driving the conversation forward together.

Maarten: Remy Overwater, thank you again for taking the time to speak with us ahead of the Leaders in Finance AI Event. I look forward to seeing you there.

Remy: Thank you, Maarten