While artificial intelligence brings massive opportunities, it also introduces serious ethical and compliance risks, especially when it comes to data privacy, algorithmic bias, and cybersecurity.
According to McKinsey, generative AI alone could contribute up to $4.4 trillion in annual global profits. Studies show that AI can improve productivity by as much as 66%. In insurance, chatbots and virtual assistants are now offering 24/7 support, cutting wait times and improving customer satisfaction. Behind the scenes, algorithms are analyzing vast datasets to better assess risk and detect fraud. Claims that used to take days can now be processed in hours – sometimes even minutes – with AI-powered damage assessments and automation. Internally, insurtechs are using AI to improve onboarding, tailor employee training, and streamline HR processes.
These are powerful shifts. But they also raise tough questions. How do we ensure the data being used is handled ethically? How do we guard against discrimination in pricing or hiring algorithms? What happens when a customer is denied coverage based on an opaque machine-learning model?
These aren't theoretical concerns. According to KPMG's most recent CEO Outlook Survey, more than half of executives pointed to ethics as one of their top worries around AI. That's especially true in insurance, where fairness and transparency are essential; people's financial futures often depend on it.
That means insurtechs need to think carefully about how AI decisions are made and how they're explained. Customers deserve to know when a bot or model is involved in determining their premiums or claim payouts. Employees must understand how performance data is being tracked and used. And both groups need reassurance that their data is being protected.
Beyond ethics, there's a growing threat from cyberattacks. With AI systems increasingly integrated into core operations, the attack surface is expanding. Insurtechs handle enormous volumes of personal and financial data – prime targets for cybercriminals. The stakes are high, especially under regulations like GDPR, which carry heavy penalties for non-compliance.
So, how can companies stay ahead of both the innovation curve and the compliance curve?
One answer lies in global standards, specifically ISO 42001 and ISO 27001. ISO 42001 is a new framework designed to help organizations govern AI responsibly. It offers guidance on managing risk, ensuring transparency, preventing bias, and embedding ethical practices across the AI lifecycle. For companies building and deploying AI systems, it's a powerful tool to operationalize responsible innovation.
Meanwhile, ISO 27001 focuses on information security. It's been around longer but is just as critical – especially for insurtechs handling sensitive customer data. This standard helps organizations identify and treat information security risks, implement appropriate safeguards, and respond to incidents quickly and effectively.
Used together, these standards provide a strong foundation for AI governance and cybersecurity. But they're not plug-and-play. Each insurtech faces a unique set of risks, serves different use cases, and operates under distinct expectations. Rather than treating compliance as a checkbox exercise, the best approach is to align it with the practical needs of the business and its customers.
As AI becomes more deeply woven into the fabric of the insurance industry, the expectations around its responsible use will only increase. Regulatory scrutiny will intensify. Cyber risks will grow more sophisticated. And stakeholders – from customers to regulators to employees – will demand greater transparency, fairness, and accountability.
Insurtechs that recognize this shift and take steps now will be better positioned for long-term success. Clear governance, ethical AI practices, and alignment with international standards like ISO 42001 and ISO 27001 are no longer just best practices. They're quickly becoming competitive requirements.
