This paper assesses whether artificial intelligence can effectively improve fraud detection and claim management, and compares leading industry solutions and approaches. Artificial intelligence is not new to the insur-ance industry, but it has the potential to be further developed to achieve greater efficiency, minimize claim payouts, and ultimately help keep premiums low and support economic growth. At the same time, industry secrecy and non-standardized metrics make it difficult to objectively assess the value of AI-based fraud detection solutions.

This study combines literature, benchmarking of leading fraud detection solutions, and qualitative in-terviews with insurance companies. Case studies from multiple insurers were analyzed to compare AI methodologies, implementation strategies, and financial outcomes. The emphasis lies in the pre-mium development, claims development, as well as the loss ratio, the burden of claims on premi-ums.

The findings show that the insurers using AI -AI-supported fraud detection consistently achieve im-proved fraud identifications, faster claims processing, and financial benefits. While statistical signifi-cance cannot be established due to limited data transparency, all insurers reported positive econom-ic benefits, including reductions in loss ratios. Machine learning AI outperforms traditional rule-based systems in scalability, adaptability, and the detection of emerging fraud patterns. However, success depends less on the technology itself and more on data quality, system integration, and organiza-tional readiness to invest in advanced technologies. Best results are achieved when AI is positioned strategically and contributes to the digital transformation of a company.