The hype around AI and its potential is extensive, so what is an insurer to do to bring AI to their businesses, asks Paul Eaton, managing director in the analytics division of Aon’s Reinsurance Solutions business.

Start by not fretting. We propose two considerations to facilitate a sleep-at-night perspective. First, insurers are already good at AI or its precursor technologies. The applicability of AI in the present and near future is entirely based on narrow AI technologies. For example, natural language processing and image recognition are both machine learning implementations with working business applications right now. Both use predictive models to achieve results. The software may be artificial neural networks trained on vast data sets, but they are nonetheless conceptually compatible with things insurance carriers have used for years, like actuarial pricing models. The point is that the application of AI is an incremental step forward in the types of models and data already applied in the business.

Second, sorting through the hype requires a staple of good business decision making: the risk-cost-benefit analysis. Determining which technologies are worth investment is within scope for decision makers that otherwise know how to make selective investments in growing the capabilities of their firm. The problems faced by a carrier are much bigger than sorting out AI if management lacks the basic skill set for making business investments. Providing an inventory of every application of AI is beyond the scope of this article. DeepIndex provides a list of 405 at deepindex.org, from playing the Atari 2600 to spotting forged artworks. Instead, suppose that AI, like electricity, will be broadly applicable across industries and functions, including the components of the insurance value chain from distribution to pricing and underwriting to claims. The goal is to identify and implement the AI empowered solutions that will further a competitive advantage. Our view is that carriers’ success with AI requires three key ingredients: data, infrastructure, and talent.

Data: AI might be considered the key that unlocks the door of big data. Many of the modeling techniques that fall under the AI umbrella are classification algorithms that are data hungry. Unlocking the power of these methods requires sufficient volume of training data. Data takes several forms. First, there are third party data sources that are considered external to the insurance industry. Aerial imagery (and the processing thereof) to determine building characteristics or estimate post-catastrophe claims potential are easy examples. Same with the vast quantities of behavioral data built on the interactions of users with digital platforms like social media and web search. Closer to home, insurance has long been an industry of data and carriers are presumed to have meaningful datasets in claims, applications, and marketing, among others.

Infrastructure: Accessing the data to feed the AI requires a working infrastructure. How successfully can you ingest external data sources? How disparate and unstructured can those sources be? Cloud computing is not necessarily a prerequisite to successful AI, but access to vast, scalable infrastructure is enabling. Are your information systems equipped, including security vetting, to do modeling in the cloud? Can you extract your internal data into forms that are ready to be processed using advanced modeling techniques? Or are you running siloed legacy systems that prevent using your proprietary data in novel ways?

Talent: Add data science to the list of AI-related buzzwords. We claimed earlier that many of the advancements attributed to narrow AI are predictive models conceptually like modeling techniques already used in the insurance industry. However, the fact that your pricing actuary conceptually appreciates an artificial neural net built for fraud detection using behavioral data does not mean you have the in-house expertise to build such a model. Investments in recruiting, training, and retaining the right talent will provide two clear benefits. The first benefit is being better equipped to do the risk-cost-benefit analysis of which data and methods to explore. The second is having the ability to test and, ultimately, implement. If your business doesn’t have the data, or the infrastructure, or the talent to bring the newest technologies to bear, then perhaps consider partnering with a firm that does.