A cross-banking, artificial intelligence is creating tremendous opportunities to improve efficiencies and operations. In compliance, where increasing requirements can lead to rising costs, machine learning is helping share the load. AI is also improving the effectiveness in the fight against financial crime by offering large data sets that can quickly be analysed. To explore how much AI is already being used in compliance, I'm joined by Seller Franzen, Group Chief Data Scientist at SEB and David Hardoon, Special Advisor, Artificial Intelligence at the Monetary Authority of Singapore. Welcome to both to Cybos TV. I'm going to start with Seller. Now you're both appearing at the Cybos conference session, looking at the application of artificial intelligence in this field. How is AI augmenting compliance practices? This is one of my favourite areas in the world. I can just see so many benefits of using new kinds of techniques in monitoring and compliance work. One of the things that we've been working on quite hard is improving data quality by using machine learning algorithms. Instead of doing the manual stuff that you did in the 70s, we can really try to bring everything into the future instead. And then of course, our clients don't meet us physically anymore. They meet us much more digitally. And then it's important for us to be able to understand their behaviours in the digital landscape as well. So much has changed even in a short few years. What are the challenges, would you say, to the industry that the industry is facing? And why is now the right time for AI? I think the right time is now because technology is there. We have the technology, we have the data. Researchers have been working on finding new techniques and doing really heavy base research for years and years. So, I mean, AI has been around for 50 years in a theoretical perspective. But now that we have the technology, we can really release the power of that. David, what are some of the real-life applications of AI in financial crime prevention? But we're already seeing quite a significant use of AI, leveraging on existing financial data as well as the more new types of data across fraud detection to really help make it seamless in real time. But it goes beyond just banking. For example, even within the central bank, in the Manitura-Thoreyves Singapore, we've built an AI-based solution called Apollo, which is leveraging on market data, transactional data to identify market manipulation. Then just to give a last example, things which, well, it's a bit of a pain for all of us, but it's extremely important to know your customer, customer due diligence, leveraging artificial intelligence to really be able to identify who the customers are. So, what are the big opportunities ahead for AI in compliance? And where will we see the greatest benefit, do you think? Well, it's a bit of a difficult question, but if I could say there's two key areas where there's great opportunity. And perhaps even both of them are a bit unusual to certain extent. Well, the first one is, well, regulatory requirements, legal implication are quite tedious, the quite lengthy. So, in fact, there's immense opportunity in leveraging an AI to analyze that and provide potential standards, both from an implementation point of view, as well as creating bite-sized human readable interpretations of these for people across the organization to use it. Secondly, and this is a bit to the core of the organization, is how do we shift from an audit-based approach, leveraging an AI on the data for either real-time or near-real-time identifying non-compliance? So, moving from order to preventive effectively. Sala, what are the prerequisites? And perhaps what's holding businesses back from applying these new techniques? So, I think the prerequisites are definitely having your data in good order, having it well-governed and managed. And then I think it's all about finding the people that love data in your organization. There's always people like that. So, identifying them and giving them the opportunity to grow and to learn new things and seeing to it that they have the support from the organization, from management, and from their teams, that they can really sort of learn to build new tools and also roll them out into the organization. Perhaps a difficult question. Could wrong AI application lead to unexpected consequences and systems with implicit bias? Absolutely. I mean, I think one of the things that makes it easy to sleep at night today is that the sort of maturity of using data for AI is still not there. It's still quite difficult to build a real machine learning algorithm that's sustainable, which means that it's not sort of the risk of doing really stupid stuff isn't really as big as it would be if it was just sort of pulling algorithms from a shelf and putting them into production. You have to do a lot of work. Because I mean, the machine learns what the data tells it and no more. It doesn't have creativity in that sense. So, if you give it bad data, then it's going to take bad decisions. David, how has AI changed the way compliance professionals are trained? Well, it has definitely changed it because if you think about it, compliance officers, public compliance professionals are at the heart of any deployment or operationalization of AI. So in a certain effect, effectively, there are the key drivers of innovation when it comes to AI across an organization. So when it comes to training, it is critical and important to train them to balance between the regulatory compliance side of the house and the innovation side of the house. Does everyone have to be an AI expert nowadays? I get asked that quite regularly. I absolutely don't think everyone has to become an expert in AI. However, there is a necessity to at least understand how the usage and the outcomes of AI can help in every single process across an organization. But expert pay me not. From a regulatory perspective, have regulators been supporting these new initiatives? I think so. I think this has kind of, it's been breaking a little bit new ground. And it's taken a little bit of time to find the good way of collaborating and learning together. Because I think that's sort of the key that everybody understands what we're doing and we can keep the trust. This is a very difficult question to answer now. I'm going to give it back to Salah. Where do you think we are within our AI journey? Is there much more to come? Oh, yes. I think we're just at the beginning. I mean, getting AI to be an integrated part of our daily business, that's sort of the holy grail. That's what I'm really hoping for. And the only way to do that is to allow our employees to be part of the journey. And David, as we've been discussing, AI is clearly the way forward in augmenting compliance processes. But how far can this actually go? So we ever see an end to human intelligence making the final decision on this. I think in no matter how far we can, or even fact, should go, it's critical to make sure that artificial intelligence, AI, is always there to augment and support human decision by design. What I mean by that is that it's critical for us at this juncture to remember that when we're going about designing algorithms, we're not designing to replace us. And if we do that, it's kind of expected that at some point in the future, decisions may be done by machines. So we need to make sure it's augmented intelligence, there to support human's decision. Well, I think we've taken quite enough of your time. There is so much more to see at Cybers 2019. Thank you very much for joining us. Salah Franz and Group Chief Data Scientist at SEB. And David Hardoon, Special Advisor, Artificial Intelligence at the Monetary Authority of Singapore. Thank you very much, guys. Thank you.