Michael Natusch, Prudential
Michael is the Global Head of AI in the Group Digital team of Prudential plc. He joined Prudential from Silicon Valley based Pivotal Labs where he led the Data Science team. His experience lies in the application of artificial intelligence methods to large-scale, multi-structured data sets, in particular neural network based deep learning techniques. Michael previously founded and sold a ‘Silicon Roundabout’ based startup and prior to that was a partner at a major consulting firm.
Michael holds a PhD in theoretical physics from the University of Cambridge and is a Fellow of the Royal Statistical Society. Capgemini’s Digital Transformation Institute spoke to Michael to understand how AI is helping Prudential in a variety of ways already.
AI at Prudential – much more than a cost play
What does AI mean for you?
In her book Artificial Intelligence, Elaine Rich says that AI is the study of how to make computers do things that people, for the moment, are better at. This is the number one thing for me: it is not about automation. When people talk about automation, they largely do so with cost in mind. AI is more importantly about scalability and customer experience primarily, and given we are in financial services, about regulation and compliance. We want to be compliant by design, and that is much more easily achieved when you have computers do tasks than humans. AI is all about customer experience, scalability, compliance by design, and then cost. In this order.
What are some of the AI Initiatives that you have launched?
We have developed a robo advisor, which we launched in our Taiwanese business early this year. As an organization, we employ around 600,000 financial advisers around the world. We already have a lot of information and historic data about the customer and financial markets. We want to use this data to provide sensible and tailored suggestions to our customers. It’s important to point out that we don’t want to replace our advisors – they are a really important route to market for us. But we want to augment them and add to their capabilities. We want our human advisors to become prime users of our robo advisor, so they can tune in even better to the actual needs of their customers. What we really want to do is to use humans to the best of their capabilities. AI is taking away the time humans previously spent on repetitive issues and allowing them to focus on where human intelligence can drive value – for both themselves and for customers. It is also why we call AI ‘augmented intelligence’.
Employees welcome AI that helps them
How have employees reacted to these technologies?
We are clear on what we want to achieve – we have no interest in killing jobs. We know that the people who work for us are extremely well-trained, understand our products, and understand our customers. We want to use those team members to the best of their capabilities. When we implemented an intelligent voice box for compliance purposes, we expected a lot of apprehension and resistance. Surprisingly, employees were quite enthusiastic. The reality of their job is that they spent a lot of time on real humdrum issues that really don’t require any of their skill and knowledge. For instance, they spent four minutes on every call just doing identification and verification of the caller. And they told us – ‘look, if your box gives us those four minutes of a call back, that would already be an amazing advantage’.
Selling AI into the business: prototyping and a focus on demonstrable successes
Can you give us a sense of the investment that was required before you saw benefits and returns?
‘No’ is the simple answer, though not for reasons of confidentiality. It is because we do not have an overall business case for artificial intelligence. We try and build ourselves into every single project and activity that is going on and influence the business case as it happens. In a highly federated organization such as Prudential, our role is to ensure that the business owners see the value in what we are trying to achieve and fund it.
How do you convince business owners of the merits of investing in an AI-driven solution?
We have a three-step process. Firstly, we have a strict no PowerPoint policy. If we are talking to an executive, we need to have a working demo. Secondly, we build on existing successes that we’ve had in other parts of the organization. And the third, and most important part, is how we make rapid prototyping a key part of our approach. We put together a “hot house” where we pull a team of people from across the organization, give them three days and a set task and have daily demos. The team comes up with a draft working prototype that the judges of the hot house – typically the business owners of the problem – can see. They can then take a clear call on whether it works or doesn’t. If it is on the right lines, we get the remainder of the 90-day cycle to develop it and make sure it actually works. This is exactly how we got the go-ahead for our regulatory voice box initiative.
Biggest challenge – getting people comfortable with robots
What were the biggest challenges you faced in rolling out these initiatives?
There are two major challenges.
Firstly, how to deal with robots? How to get used to the real-time nature of using software and seeing feedback coming in after changing a few parameters? How to overcome skepticism towards AI-driven solutions?
The second challenge is more technical in nature, e.g. take a call on data availability, or the platform/ technique you want to use.
Do you see the need to develop talent and skills as a key challenge?
We have a structured approach to enhancing digital skills. For instance, we are running a training program for employees from all BUs to learn Alexa programming skills. The primary objective is not to develop AI solutions, but we are trying to increase the level of confidence that our colleagues have with AI. We hope to build an understanding of what AI both can and cannot do, as they are equally important. But one thing we are not going to do is go on a big hiring spree and try to hire up all the AI experts around the world. Instead, we will focus on hiring people with the right mathematical background and aptitude to understand our problems, our data, and our customers.
 Artificial Intelligence, Elaine Rich and Kevin Knight, 1991.