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This article originally appeared on Insurance Day, February 2024.
AI technology is bringing rapid advances to the insurance industry, offering many opportunities to meet evolving customer needs and improve efficiency.
The latest advances in artificial intelligence (AI) and related innovations demonstrate the urgent need to embrace digital transformation.
Innovators in the insurance industry need to be more astute than ever in anticipating new and evolving threats. Changing customer expectations require companies to go beyond just providing and repeating coverage for historical losses to offering new proactive solutions to new risks.
AI technology offers many opportunities for the insurance industry to meet these demands and bring rapid advancements to the insurance industry. Enables increased productivity and efficiency. Innovations are being developed across the insurance supply chain and in various product areas.
Over the past decade, the insurance industry has seen unprecedented investments aimed at streamlining the insurance value chain. In 2022 alone, he invested $7.9 billion in 521 completed transactions around the world, and since 2012, almost $50 billion has been invested worldwide.
While there are significant benefits from this potentially transformative technology in insurance, it also raises many legal issues and requires a careful balance between risk and benefit.
These issues include concerns about data privacy, bias, and discrimination. Data use and data privacy are particularly important in how consumers view insurance innovation.
customer data
Many insurance innovations are built on the ability to collect and assess more customer data, enabling more accurate underwriting, faster claims decisions, and Apply insurance to previously difficult risks.
In response, insurers should expect increased scrutiny of the extent to which customers trust and want their personal data to be used to make decisions.
Although the EU’s General Data Protection Regulation has now been adopted as standard data protection practice almost worldwide, consumers still have concerns about what data is being tracked and how it is being used. This is the biggest concern.
How companies use consumer data to develop better insurance products provides the evidence needed to build and verify trust, especially under regulatory regimes such as the UK’s Financial Conduct Authority. This will become more and more common.
Marketability criteria for new devices and technologies is also an important consideration for companies. The device must meet essential requirements and safety characteristics, including those stipulated by EU harmonization legislation. It is also necessary to assess whether connected devices and technologies are protected by intellectual property rights.
Overall, we should expect consumers to be more willing to take action, including turning to regulators, when insurers are seen to be exceeding standards.
regulatory risk
Meeting regulatory requirements is another risk in itself. As technology and innovation have uprooted the insurance industry over the past decade, regulators have increasingly been left behind to keep up with the latest product developments and, by extension, regulatory implications.
As such, regulatory regime approaches around the world remain heterogeneous, and many companies are looking to how first movers seek to define sensible regulatory regimes that remain attractive for innovation. While the EU is undoubtedly leading the way with EU AI legislation, other countries have approaches that are more focused on providing guidance and principles, such as Singapore.
A balance between ensuring that regulation provides adequate protection for customers (particularly consumers and small businesses) and maintaining attractive inward investment prospects to foster innovative start-ups and a talent base. Finding out has become a challenge that regulators must adjust to. And companies will need to change direction.
To reduce risk and realize the potential of AI technology, insurers must create a robust AI governance framework that ensures best practices. This includes data governance, explainability, transparency, and continuous monitoring.
Doing so sends a strong message to customers, and the broader market, that our commitment to promoting responsible AI practices goes hand in hand with the application of AI ethical principles in relation to AI technology.
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