Extracting value with algorithms

17-01-17 12:00 | Digital banking | The Digital Hub

How data science is transforming banking

Data science allows us to stay one step ahead of our customers, to predict and solve their problems even before they experience them. We want to surprise and delight our customers by going beyond their expectations. We want them to ask us: “How did they do it?”

Today we can do something we could not do many years ago. Using real-time machine learning and algorithms-driven solutions, we can collect and analyse a large variety of data our clients use when making important decisions. When this rich and abundant data is correctly connected and analysed, it provides a business with tremendous value and strategic insights. This allows us to customise and improve the customer experience.

My philosophy is that data science starts and ends with the individual. Modern data science is about data systems, machine learning, algorithms and big data, but most of all, it’s about individual choice and behaviour. 

It’s important for me to emphasize that data science is not some sort of “big brother” who is watching you. It’s about making your life easier by giving you the most relevant information that meets your needs at any given point in time - and of course we always make sure to meet all legal requirements when using or processing any data. 

Given today’s abundance of information, people may be hit by what psychology professor Barry Schwartz calls “choice paralysis”. That’s why one of data science’s most important contributions to individuals and organisations is solving the complexity of choices resulting from information overload. 

Leonardo da Vinci once said: “Simplicity is the ultimate sophistication.” That’s something many companies should start striving towards when designing customer experiences – and this is a core element of the journey we are embarking upon here at Nordea.

/Carmine Gioia, Head of the Data Science and Analytics Department at Nordea. 

Fact box: Intelligent algorithms

At Nordea, we’re using data science to develop products and services that we hope will soon make a difference in our customers’ lives. Instant customer feedback about their experiences allows us to optimise development to best meet their needs. Here are a few things we’re working on:

  • Implementing intelligent recommendation systems, based on machine learning and artificial intelligence, that help customers with financial decision-making.

  • Experimenting with modern technologies such as eye tracking, analysis of facial expressions and emotions, and ethnographic techniques to improve mobile banking, online banking, client advisory interactions and online trading.

  • Customising online content based on a customer’s behaviour and interests.

  • Testing customer behaviour using advanced design of experiments to reduce latencies and waiting times and providing easy access to products, services and relevant information to become more effective in serving our customers.

  • Using algorithms to prevent fraud and money laundering.

Profile: Carmine Gioia is Nordea’s new Chief Data Scientist

Carmine Gioia is Nordea’s new Chief Data Scientist

Carmine Gioia recently joined Nordea as head of the Data Science and Analytics Department, which consists of more than 100 highly skilled, dedicated data scientists. Carmine, with 20 years of experience in data science, comes to us from Saxo Bank, where he was Chief Data Scientist & Analytics Officer and Group Global Head of Data Science and Strategy. He was also the former Chief Data Scientist for Oticon AS, part of William Demant Holding. Carmine holds a PhD in Micro-econometrics and has advised some of the world’s most successful companies. He has been working with the Data Science, Big Data, and Design of Experiments at the Massachusetts Institute of Technology (MIT), where he also served as an associate professor, scientist and researcher at the School of Engineering. The Italian academic runs a small non-profit Aikido practice and enjoys practicing Aikido and Taekwondo with his two children. In his spare time, he likes to programme Deep Learning Algorithms in Python and hopes to get his wife and children excited about this hobby as well.

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