With this digital transformation, intelligence driven by machines has infiltrated our lives—at home, in our cars, and at work. And it’s just getting started. The global AI market size is projected by MarketsandMarkets to grow from $58.3 billion in 2021 to $309.6 billion by 2026.
This growing reliance on technology requires insurers to rethink how they assess risk, adapt to changing customer expectations, and introduce new products. The majority of insurers are no strangers to the world of AI technology, but the influx of unstructured data now available online, and documented in valuable collections like existing claims and customer files, medical records, and consumer wearables can be daunting. Tracking data, labeling it correctly, training systems effectively, interpreting outcomes accurately, and applying the technology in a productive way requires extensive resources and expertise. To realize the full value of data as an asset takes more than the right algorithm. It requires contextual understanding and human intervention.
Augmenting algorithms with humans
In a sector abundant with customer insights long utilized to assess risk, it’s only natural to incorporate AI technologies such as machine learning, natural language processing, and deep learning to improve the accuracy of predictions. But the technology itself is not enough. An algorithm can handle massive calculations and perpetual, repeated tasks at unprecedented speed, but it fails to grasp the greater context through rationale and judgment the way a human does.
The industry is oiled and ready for AI, yet many insurers still haven’t realized its value. The key to AI for insurance is thinking of data as an asset and understanding that our behaviors are becoming easier to track in real time. And it’s bringing in expert data scientists to roll it out responsibly through something called experiential AI.
Experiential AI, which uses human-guided algorithms to solve real world problems, can help insurers integrate AI into existing technology for faster, more accurate data quality, privacy, and infrastructure compatibility. This means marrying theory with practice to align companies with growing regulatory frameworks and responsible AI practices, leveraging new technologies, answering questions, providing oversight, and sharing industry standards to innovate responsibly as the industry grows. Today, insurers should also be using AI natural language processing to ensure compliance by filtering alerts about ever-changing rules and regulations of AI itself—from new proposals for federal regulations to a regional law that passed in New York City last fall restricting the use of AI in employment decisions.
Insurance meets experiential AI
While most insurance companies still face obstacles of siloed customer data between departments, leading to unproductive and costly mistakes, the industry should be moving toward experiential AI. Every siloed department, from fraud detection in claims processing to underwriting, distribution, pricing, cybersecurity, and customer service, stands to benefit from meaningful human-centric experiential AI to assess risk faster, automate workflows, assign human tasks, and enhance the customer experience. MarketWatch predicts the AI insurance market will grow steadily between 2021 and 2027, so it’s only a matter of time before customers expect this kind of service.
AI in insurance will impact millions across the globe, so it’s paramount that insurers minimize harmful pitfalls like data breaches, ransomware attacks, and biases by working continuously to implement algorithms responsibly. That means checking for biases, ensuring feedback between users and the machine, and avoiding non-interpretable output, known in AI as a black box, through the transparency of data so insurers understand why machines make the decisions they do.