This project explores how a wide range of AI techniques can be applied in practice to solve real business problems.

The focus is on end-to-end insurance processes with the overall goal of improving efficiency, transparency, customer experience, and data-driven decision-making. We explore the use of different techniques such as process mining, AI agents, large language models (LLMs), machine learning (ML), robotic process automation (RPA), and broader digital transformation methods.

Insurance companies handle large volumes of complex information: customer data, policies, claims, risk assessments, incident reports, documents, and multichannel interactions. These processes are often fragmented across legacy systems, manual routines, and diverse organizational units, making it difficult to gain a holistic view and to systematically improve operations.

This project, conducted in close collaboration with If Insurance, investigates how AI can drive business process transformation in the insurance sector. The project already includes, and in future will further expand, the use of process mining, AI agents, LLMs, ML models, RPA solutions and digital transformation frameworks to:

• Analyze and redesign insurance processes.
• Support intelligent automation and decision support while maintaining compliance and fairness.
• Improve customer experience through smarter, more responsive and transparent services.

The project is closely connected to research at the Department of Computer and Systems Sciences (DSV), especially topics related to business process management, process intelligence, AI, and digital transformation.

Please note: Interested students and collaborators should contact Amin Jalali, rather than contacting If Insurance colleagues directly. All requests will be coordinated through Stockholm University.