The skinny on the AI technology landscape
Part 1 in our Sea Change AI series.
We often hear the term Artificial Intelligence banded around as common parlance these days. Much in the way you may have heard “the internet” being used in the past couple of decades, the term “Artificial Intelligence” is used to describe a collection of technologies that make up a new age of advancement in computing capability. In this article, we set out to cut through some of the jargon, provide a comparison of some of the common types of AI, and how they are currently being used in the global Insurance markets.
Deep Learning is a subset of Machine Learning that uses neural networks with many layers (hence the "deep") to model complex patterns in data. It is mostly used in image and speech recognition, autonomous vehicles, medical diagnosis, and more. This technology scores high on accuracy in tasks like image and speech recognition due to its ability to learn from very large amounts of data. However, it requires significant computational resources and large datasets for training, so not scoring so high on the sustainability and ESG front.
Generative AI, or GenAI, creates new content, such as text, images, or music, based on learned patterns from existing data. It has gained a reputation for the type of AI that is closest to making autonomous decisions or “thinking”. Its practical applications are content creation in text and images, new drug discovery, and personalised marketing. Gen AI can generate creative and novel outputs, enhancing productivity and innovation of its human operators. As such it has gained a reputation for enhancing performance in firms. As it is a relatively new technology category, there are challenges with harnessing this type of AI such as the potential for its misuse (e.g., deepfakes), ethical concerns, and commercial issues with intellectual property and ownership.
Natural Language Processing, or NLP, is a field of AI focused on the interaction between computers and human language, NLP techniques enable machines to understand, interpret, and generate human language. NLP technology is seen in Chatbots, language translation, sentiment analysis, and voice assistants. It can enhance communication between humans and machines, improve the customer service experience, and improve efficiency by automating text-based tasks. However, handling the nuances and complexities of human language, such as context, sarcasm, and idioms can sometimes be beyond current NLP technology, although the competency is reported as improving all the time.
AI technologies work together to enable machines to perform tasks that typically require human intelligence. Researchers and developers are continually pushing the boundaries of what AI can do and there are additional fields emerging all the time such as Explainable AI (XAI), Edge AI, Quantum Machine Learning and Federated Learning.
Many Insurance firms have at least dipped a toe in the AI waters in recent years. Databridge valued the global AI in insurance market at $4.59 billion in 2022 with projections to reach approximately $79.86 billion by 2032.
North America led the market in 2022, driven by major insurance companies like Prudential Financial, MetLife, and Berkshire Hathaway. AI is widely used in insurance for underwriting, fraud detection, claims processing, customer service, enhancing efficiency and decision-making. The demand for hyper-personalised services and efficient risk management remain key drivers of AI adoption in the insurance industry.