Four AI-Related Trends Shaping Insurance
AI’s influence is already everywherein our offices, homes, cars, and even on our wrists. When COVID-19 disrupted global markets, it simultaneously accelerated digitization for insurers. Virtually overnight, companies had to support remote workforces, expand digital channels, and upgrade online capabilities. While many organizations didn’t invest heavily in AI immediately, the shift toward digital solutions has put them in a strong position to integrate AI into their operations shortly.
Below are four core technology trends deeply intertwined with AI that promise to reshape the insurance industry over the next decade.
Exhibit 1
Artificial Intelligence can deliver Carrier’s expectations through machine learning and deep learning.
Artificial Intelligence
Transforming human-like cognition into powerful computational capabilities.
Machine Learning
Enabling AI by using data-driven models that adapt and evolve with experience.
Deep learning
Unleashing advanced insights through neural networks designed to learn from raw data.
1960s |
1970s |
1980s |
1990s |
2000s |
2010s |
2020s |
2030s |
2040s |
2050s |
Artificial Intelligence (AI)
Machines exhibit Intelligence (AI), which involves designing systems that perform tasks requiring human intelligence, such as learning, reasoning, and problem-solving at scale. By processing vast amounts of data in real-time, AI can identify patterns, automate complex processes, and generate insights that enhance decision-making across every industry, from finance and healthcare to manufacturing and beyond.
Machine Learning (ML)
Machine learning is at the core of modern AI, relying on statistical methods to identify patterns and generate predictions. Through techniques like supervised, unsupervised, and reinforcement learning, ML algorithms refine their performance based on the data they encounter. This iterative approach helps organizations tackle complex challenges in areas such as healthcare, finance, and retail, unlocking faster, more accurate decision-making.
Deep learning (DL)
Deep learning extends traditional machine learning by harnessing layered neural networks that mirror certain aspects of human cognition. These networks automatically extract and combine features at multiple levels, enabling breakthroughs in tasks like speech recognition, computer vision, and predictive analytics. The more data these models process, the more refined and accurate their outputs become—powering some of AI’s most transformative solutions.
Four core technology trends, tightly coupled with (and sometimes enabled by) AI, will reshape the insurance industry over the next decade.
Explosion of data from connected devices
Connected consumer devices are on the rise. While industrial sensors have been omnipresent for years, everyday items such as cars, fitness trackers, home assistants, and smart watches are joining the Internet of Things (IoT). New categories like connected clothing, eyewear, appliances, and even medical devices are rapidly emerging. Experts estimate that up to one trillion connected devices could be online by 2025, creating an avalanche of data.
For insurance carriers, this new data will bring unprecedented insight into customer behaviors and risk profiles. Personalized pricing models and real-time service delivery will become more common. Imagine a wearable device tied directly to actuarial databases; it could generate daily risk scores and anticipate events, transforming how policies are priced and updated.
Increased prevalence of physical robotics
The field of robotics has seen many exciting achievements recently, and this innovation will continue to change how humans interact with the world around them. Additive manufacturing, also known as 3-D printing, will radically reshape manufacturing and the commercial insurance products of the future. By 2025, 3-D-printed buildings will be common, and carriers will need to assess how this development changes risk assessments. In addition, programmable, autonomous drones; autonomous farming equipment; and enhanced surgical robots will all be commercially viable in the next decade. By 2030, a much larger proportion of standard vehicles will have autonomous features, such as self-driving capabilities. Carriers will need to understand how the increasing presence of robotics in everyday life and across industries will shift risk pools, change customer expectations, and enable new products and channels.
Open-source and data ecosystems
With data becoming ubiquitous, open-source protocols are emerging to standardize how information is shared across sectors. We’re likely to see public-private data ecosystems, collaborative networks where participants pool information under common regulatory and cybersecurity guidelines.
For insurers, these frameworks could enable seamless data-sharing from wearables, connected homes, and cars via major tech players like Amazon, Apple, and Google. Such interconnectivity will expand the scope of data-driven underwriting, streamline claims processes, and reduce friction in customer interactions.
Advances in cognitive technologies
Deep learning tools such as convolutional neural networks (CNNs), already used in image, voice, and text processing, are poised to evolve furtherand expand into new applications. These cognitive technologies, designed to mimic human learning and inference, will become essential for handling the immense and complex data streams generated by connected devices.
As AI models grow more sophisticated, insurers can develop “active” policies closely tied to real-time behaviors and events. Constantly learning and adapting, these solutions can respond instantly to shifts in risk or customer need screating opportunities for novel product categories, hyper-personalized services, and real-time engagement.