Data is the foundation for innovation. Recent developments in artificial intelligence (AI), for instance, wouldn’t be possible without the data on which AI is based. And the capacity for future innovation is vast: as a society, we’ve just scratched the surface of what data can do. Smart use of it unlocks immense potential, yet many organizations sit on vast amounts of data without knowing how to turn it into value.

Data is instrumental for our business at Allianz Trade: trade credit insurance essentially rests on information and intelligence. We collect and analyze financial, economic and sectoral data on 83 million companies so as to assess credit risk and protect our customers against non-payment risk so they can grow safely. As such, we want to maximize the value we can draw from data – collecting more information and more accurate data, analyzing in new ways or at a larger scale, and using data to develop predictive models for business cases.

Breaking down data silos

To make the most of data’s potential, it’s crucial for different teams within an organization to collaborate and share it. Many organizations don’t realize the extent of the data they possess, and sharing knowledge between departments can uncover untapped data sources, breaking down departmental silos in the process.

At Allianz Trade, we’ve tackled this challenge with our Data Ninjas initiative. Launched as a pilot program in Germany, it brings together our data experts from various company divisions to explore ways to utilize data effectively. The experts transparently discuss the data their departments have, as well as any restrictions on using it. By fostering cross-departmental collaboration, we aim to transform our data into valuable insights that drive business growth. As an example, within this community, we created a data map of where what kind of data flows in and where value to that data is added. But we also discuss hands-on topics such as the raw data of Net Promoter Score results and how to squeeze out actionable insights.

Data literacy for all

True data innovation requires empowering all employees, no matter their role, to understand and use it. When people understand how it works, they can more easily identify untapped sources, generate higher-quality data and use it in pioneering ways. Dispersing data analysis across the organization also frees data scientists for complex tasks such as the data science models that are being built and updated. We’ve reaped these benefits and more at Allianz Trade.

We recently hosted an internal Datathon at our German headquarters in Hamburg with several employees across different functions participating. The event was designed to make data work attractive and practical. Guided by our own data experts, participants learned how to analyze data in software programs like Excel and SAS, using programming languages such as Python and R. They also tried out our in-house generative AI tool, AllianzGPT. Teams developed data-driven projects focused on trade credit insurance, presenting to a jury of senior leaders.

Events such as the Datathon in Hamburg are one part of the picture. In the past we’ve also had a global series of Data Science Days, open to all employees, which aim to boost attendees’ understanding of data science, and are part of our commitment to upskilling.

What comes next?

Looking at the future of data, I’m particularly interested in technological convergence, or how different technologies will come together as they advance. Right now, there’s a lot of hype around generative AI tools like ChatGPT. This fits the Gartner Hype Cycle model, where new technologies go through a phase of inflated expectations, followed by disillusionment, and eventually ascend to a plateau of productivity.

What excites me is what comes after the hype. While tools based on large language models generate content, the real question is how to condense and analyze it into actionable insights.

Beyond generative AI’s uses today, its convergence with other technologies down the line will create entirely new innovations. Looking back, there were great examples how the convergence of different technologies led to news business models. Startups in the late 1990s had the same idea as the YouTube founders in 2005, but YouTube’s success required the convergence of fast and cheap internet access and the smartphone with an integrated camera that enabled user-generated content. And now, we already see the harbingers of the convergence of Gen AI and a Convolutional Neural Network’s ability to detect the mood in human faces. Imagine that your AI assistant is able to change the way it communicates with you based on what it detects what you need in that moment!

Leading innovation in trade finance

At Allianz Trade, we’re leading data innovation in trade finance to enhance our solutions and customer service. We’ve developed tools that support decision-making, boost productivity and enable quicker responses to our customers to improve their experience (for example when they request more information about a credit decision). Our innovations allow our teams to focus on what matters most – helping our customers anticipate, analyze and respond to market changes. 

Our commitment to data science and innovation truly sets us apart from other organizations. I’ve seen firsthand how effectively leveraging data elevates our customer service and creates real value.