Ermir Qeli, Head of Data Science in AI at Swiss Re, discusses challenges with unstructured data in insurance tech stacks. He shares insights on scaling systems, generative AI use cases, and fraud detection evolution in the insurance sector. The significance of incorporating unstructured data and visual data in improving fraud detection and customer service is highlighted, along with innovative use cases of video assessment in bicycle insurance claims.
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Quick takeaways
Insurance industry struggles with unstructured data, hindering analytics efficiency.
Adopting generative AI can optimize data analysis in insurance, improving processes like claims processing.
Deep dives
Challenges in the Insurance Industry Due to Unstructured Data
The insurance industry faces significant challenges due to its reliance on unstructured data, leading to inefficiencies in data analysis processes. Insurance leaders encounter difficulties in data tapping and analysis scalability due to complex IT landscapes and scattered data across systems. The need to access data sets from various systems to perform analytics tasks poses a major obstacle for insurance professionals, affecting their ability to offer efficient tools and insights to their workforce for decision-making.
Transitioning Towards AI Adoption in Insurance
Transitioning towards AI adoption in insurance requires addressing the challenges of unstructured data and modernizing tech stacks. Organizations must strike a balance between updating data stacks and leveraging advanced technologies like generative AI for optimization. While traditional ML techniques are fundamental, the inclusion of generative AI can enhance data analysis processes, particularly in unstructured data tasks like claims processing and fraud detection.
Enhancing Insurance Workflows with Data-driven Insights
Data-driven insights are revolutionizing insurance workflows by optimizing workforce allocation, enhancing customer service, and exploring innovative insurance products like parametric insurance. Leveraging machine learning algorithms enables insurers to prioritize tasks efficiently and improve claims processing experiences. The integration of large language models and machine learning technologies enhances anomaly detection and fraud prevention efforts, contributing to operational efficiencies and customer satisfaction in the insurance sector.
Today’s guest is Ermir Qeli, Head of Data Science in AI at Swiss Re. Ermir joins us on the program to talk about the array of challenges facing insurance leaders at this stage of AI adoption across the sector and how the pace for which is marked by the fundamentally unstructured data at the heart of insurance tech stacks. Throughout the episode, Ermir offers his perspective on scaling systems dealing with these and similar issues, offering numerous use cases in generative AI and beyond to assist in these processes along the way. To discover more AI use cases, best practice guides, white papers, frameworks, and more, join Emerj Plus at emerj.com/p1.
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