The Earnix Blog
May 20, 2025
By Andy How
In an increasingly complex insurance landscape, managing general agents (MGAs) must adapt by transforming their underwriting practices through technology and data-driven insights. By embracing AI and real-time data, MGAs can enhance their operational efficiencies, boost carrier confidence, and position themselves as strategic partners in a competitive market.
April 22, 2025
By Equipo de Earnix
Artificial intelligence (AI) in insurance underwriting is revolutionizing carriers’ business models by enhancing their risk assessment capabilities, allowing them to deliver highly-personalized offers, and streamlining processes across functional boundaries.
February 18, 2025
By Jimmy White
In the fast-paced and highly competitive world of insurance and banking/lending, staying ahead of the curve is more critical than ever. The Earnix Copilot can answer questions, provide recommendations on best practices, and generate code to automate your processes in the Earnix platform.
December 23, 2024
By Equipo de Earnix
The insurance industry is undergoing a significant transformation, largely due to the integration of advanced AI technologies. In this video, Adrian Coupland, Head of EMEA, Insurance, discusses how Earnix is helping insurers with AI-based platforms, the challenges posed by legacy systems, and the importance of modernization in the insurance sector.
July 15, 2024
By Equipo de Earnix
AI is revolutionizing the insurance industry today by enhancing underwriting, claims processing, customer service, and product development. Yet the rapid advancement and widespread of adoption of AI in the insurance industry leads to new questions about the potential for biases and discrimination – as well as AI’s overall implications for insurers.
June 26, 2024
By Aakash Shirodkar
The use of synthetic data has expanded greatly the options in the world of analytics and data science, and has found traction in many corners of the insurance industry. It has yet to find wide usage in specialty markets. In these markets, failure to take full advantage of synthetic data runs the risk of suboptimal decisions and the resulting negative impacts to profitability.