The insurance industry has long been associated with a conservative culture and a reluctance to embrace innovation. However, in recent years there has been a notable shift and insurance companies are demonstrating openness to innovation. Some are even drivers of innovation such as the German insurer HDI Global.
Seamless Xtra’s Paula Hallentuch had the opportunity to speak with Willi Weber, the Head of Data Analytics at HDI Global, regarding the technological trends and innovations shaping the future of the insurance industry.
Willi, you have over 5 years of experience in data science and analytics. Especially in recent years, the insurance landscape has changed a lot. What are currently the biggest innovations in the commercial insurance industry?
In the commercial insurance industry, three core innovations stand out, all underpinned by the strategic use of AI, generative AI and data analytics.
First, AI-driven portfolio management is transforming risk management by enabling insurers to predict, simulate and adapt to emerging trends in cyber threats, climate risks and changing market situations with new precision. This enables dynamic optimisation of insurance portfolios for better risk mitigation and financial performance.
Second, the application of generative AI is transforming internal processes. Through the now possible use of large volumes of unstructured data, AI algorithms can be used to optimise processes in underwriting and claims handling, which ultimately leads to increased customer satisfaction.
Finally, the focus on sustainability is increasingly being integrated into the insurance workflow, helping companies manage the complexity of ESG issues and meet their standards. This approach is helping the industry meet its responsibilities in the face of today’s challenges.
What are the challenges insurers are facing and how can augmented analytics help overcome them?
Insurers must navigate through a complex landscape of challenges that focus primarily on decision-making, particularly in the areas of underwriting and portfolio management. They are dealing with a flood of information that, while largely accessible, is difficult to sift through, identify and use effectively.
This complexity is compounded by the fact that analytical tools, which are essential for making informed decisions, often do not fit well into the daily workflows of those who need them most. And even when self-service solutions are available, they do not meet the needs of the majority. Using these solutions requires a high level of data literacy, a skill that is not available or even desired by all employees. Additionally, not everyone is willing to engage with data analysis and not everyone has the time or capacity to gain meaningful insights from these resources.
How can we ensure that these users, such as underwriters, have access to insights that are not only relevant but also actionable for their specific needs?
The goal is to enable them to make better decisions without having to become data experts. The challenge for insurers is to bridge this gap and make advanced analytics accessible and useful to everyone, regardless of their data skills. This is where the concept of Augmented Analytics comes into play. It goes beyond the traditional definition of supporting the analytics professional to supporting business users with the right insights at the right time.
By merging analytics with intelligent software engineering processes, we bring analytics where users need it, into their workflow, and ensure easy access to analytics from core systems. People get access to the insights they need, when they need them, without having to be actively involved in obtaining and processing them. It’s about giving people insight without making it a burden.
Are there any limitations to implementing augmented analytics in the insurance industry?
It’s important to understand that you can’t just integrate an augmentation with the snap of a finger. It’s not just a technical implementation; your organisation must already have a higher level of maturity in analytics transformation. It requires a holistic approach that focuses on business strategy, people, organisation, data ecosystem and culture. The technical implementation relies on established microservices concepts, and the infrastructure must be able to handle it.
In addition, identifying the right insights and relevant workflows requires extensive business engagement. Finally, while augmented analytics relieves you of the need to build extensive data literacy, it does not relieve you of the need to invest in user analytics awareness, as they ultimately need to interpret the results and initiate the right actions.
Thank you very much for your insights. We are looking forward to meeting you at Seamless Europe in Munich in September.
