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The Software-as-a-Service (SaaS) model has experienced remarkable growth in the fintech sector over the past decade. According to research from Boston Consulting Group, fintech revenues are projected to increase sixfold by 2030, reaching $1.5 trillion.

Like much of the financial services space broadly, the insurance industry is growing interested in adopting AI. The technology holds immense potential to revolutionize insurance, enhancing efficiency and accuracy across various processes such as claims and underwriting. However, concerns surrounding data privacy and integration complexities have prompted a measured approach among industry leaders.

Dozens of GPT-driven products are already on the market, and hundreds more are in development. These tools collectively aim to revolutionize traditional roles and processes, transforming them into dynamic, parallelized workflows using variants of LLM models, such as AutoGPT. Tools like GitHub Copilot, powered by OpenAI's Codex, are demonstrating significant improvements in developer productivity, with some studies showing up to a 55% increase in task completion speed. 

Over the last decade, public introductions of advanced technologies from big tech firms, such as Google Glasses and Meta's (then-Facebook) Metaverse platform, were met with infamous disappointment. In stark contrast, initial iterations of generative AI (GenAI) in large language models and other tools deployed across the global economy by smaller players like OpenAI in the last few years are having a far more lasting impact. A recent report from McKinsey noted that the staying power of GenAI could add $2.6 trillion to 4.4 trillion USD annually to the global economy.

Where new, emerging capabilities in AI mean new opportunities for financial institutions – especially those that operate financial exchanges, where data and predictive analytics are paramount – the palpable risk of failure in AI adoption initiatives can prove a significant hurdle.