AI in Insurance – Interview with Marko Sumina

Meet Marko Sumina, a seasoned expert who is passionate about using advanced technology in the insurance industry.

AI in Insurance – Interview with Marko Sumina

After eight years of navigating numerous roles in the insurance industry, he successfully migrated to Adacta as a Business Analyst. What prompted this job change? A strong ambition to use newly obtained machine learning knowledge to revolutionise the insurance industry. Marko, who is now deeply embedded in the insurtech business, throws light on the significance of Business Analysis in AI-driven projects at Adacta.

What prompted your shift to a career in Business Analysis, specifically within the insurtech industry?
My transition to Adacta as a Business Analyst was driven by a profound aspiration to apply the machine learning knowledge acquired during my studies to innovate within the insurance domain. Joining the IT industry seemed the most fitting avenue to achieve this goal.

As a Business Analyst, how do you perceive the role of AI in enhancing decision-making and business processes within the insurance industry?
The insurance industry holds significant potential for the effective application of AI. However, it's crucial to establish the foundational building blocks before delving into model evelopment and testing ideas. Initiating the process requires well-structured and meticulously designed data storages, followed by the implementation of an AI infrastructure. Only after these steps are in place can you effectively apply algorithms to address specific business use cases.

In your experience, what are some key ways AI is currently being utilised in the insurance sector, and how do these applications impact the industry's overall performance?
AI finds various applications in the insurance sector, with a primary focus on claims-related processes. This emphasis is due to the prevalence of paper-based and insufficiently digitized claims procedures. Another contributing factor is the direct impact that the efficiency of underwriting and claims processing has on a company's profitability. Beyond claims, AI is also applied in non-claim-related areas such as enhancing policyholder loyalty, upselling and profiling, metadata analysis, IoT applications, chatbots, telematics, and agent matching. Business Analysts often deal with data-driven insights.

What are the challenges and opportunities you've encountered when analysing AI-generated data and translating it into actionable recommendations for insurers?
Always consider the specific business problem you are addressing before collecting data and testing your ideas. Prioritize the end user experience to ensure usefulness and presentability. Your work is truly complete only when end users can make actionable decisions based on the solutions you've implemented.

How do you envision the future of AI adoption within the insurance sector, particularly concerning data analytics and business process optimisation?
AI adoption in the insurance industry has progressed relatively slowly compared to sectors like banking. Despite significant investments in insurtech startups, the outcomes have been limited. This is primarily due to challenges such as the insufficient domain knowledge of startups focusing on generic AI applications. Additionally, the adoption hurdles are exacerbated by the presence of large legacy systems within insurance companies that require redesigning to align with these specific purposes.

ChatGPT is a widely discussed AI tool. From a Business Analyst's perspective, how do you think conversational AI like ChatGPT can change or enhance customer interactions and decision-making in the insurance domain?
Embracing these tools is highly beneficial as they expedite the research process and alleviate concerns associated with widespread AI adoption. Comparable tools are already in use for client-related and internal company chatbots. They aid clients and employees in searching, engaging in conversations, and applying Large Language Models (LLMs) to enhance their work processes.

Have you explored the potential use of ChatGPT or similar AI tools in improving customer service or data analysis for insurance companies? If so, could you share any insights or experiences?
We've primarily delved into the potential applications of these technologies for internal purposes, particularly in the realm of intelligent document search and coding assistants for our developers. However, we continue to explore new possibilities on a daily basis.

AI's role in software development is evolving rapidly. How do you perceive the impact of AI on the Business Analysis field, and how might it shape your role in the coming years?
For a business analyst, it serves as an excellent aid by offering a tool to comprehend complex subjects more rapidly than was previously achievable. Being an international company, there are substantial advantages in using it to overcome potential language barriers as well.

Do you believe that humans can still bring unique qualities and decision-making abilities to the table that AI cannot replicate, particularly in the context of Business Analysis within the insurance industry?If so, what are some examples of these human strengths?
Predicting anything that AI cannot replace is challenging. The current level of adoption and research is unprecedented and was not anticipated nearly a decade ago, surprising even the most optimistic individuals in the field. Even aspects inherently considered human, like creativity, are gradually being replaced by AI-related applications.

We encourage you to visit our website to learn more about Marko's journey and Adacta's meaningful projects in the field of insurtech. Visit our virtual stand at the ongoing job fair for an interactive experience and a deeper look at the great prospects inside our organisation.

Your curiosity will lead you to a world of innovation and expertise!

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