There is so much going on that has potential to improve the way that insurers operate, it’s hard to know which opportunities to address, and also, which risks are too important to ignore.

At SAS, we are fortunate to have a team of industry experts who understand how to contextualise the coming artificial intelligence (AI) opportunities for insurers. This is my summary of various ideas they have published, to help you cut through the hype….

Insurance and artificial intelligence

Our recent SASchat discussed the readiness of the insurance ecosystem for artificial intelligence (AI). There was a strong feeling that the insurance industry could really benefit from AI and machine learning, with use cases including claims fraud prevention, and the idea that AI would improve efficiency across the whole process, from underwriting through to claims. Health insurance would also benefit from the increase in data from wearables about fitness, and many health insurers are already starting down the ‘wellness’ route.

There is also potential for huge improvements in customer experience, not least through the use of chatbots. Chatbots could free up customer service teams to focus on the more difficult cases, by handling the easier calls. This would reduce waiting times, and therefore improve customer satisfaction.

There are other insights into the use of AI in insurance in this recent report on the Insurance AI and Analytics Europe event, focusing on the experience of insurance companies in Europe.

What about the impact of the Internet of Things? This is closely connected to the coming of AI, because it is really the increase in data from connected items that is driving the need for advanced analytics. Connected devices, for example, can tell insurers about the way people drive, so that careful drivers can be given reduced premiums. There are plenty more examples in this article on IoT and insurance.

The insurance ecosystem is both excited about the potential of AI in insurance, and concerned about how far the disruption will go. Read this report by SAS insurance experts on the scale of change ahead.

The insurance industry is not exempt from the move towards personalisation, either. The idea of a ‘segment of one’ is rapidly catching on, with real-time analytics supported by AI technology being a key part of that move. The thinking is that data can be used to improve the customer experience, and also to offer hyper-personalised products. There are some concerns about how this will fit with data protection law, and also a question, still unresolved, about how this would affect risk pooling.

New models of delivery

Might one possible answer to this conundrum be to offer insurance in return for data? This question was prompted by the opening of a new gym in Germany with a membership structure based on free gym usage in return for access to data. If insurance was offered on this basis, there would be no question about consent, after all. The real question, though, is how insurers would monetise the data, and whether they could generate big enough returns from it to make the model viable.

Can a new model of delivery be to offer #insurance in return for data? #AI Click To Tweet

Fraud is a huge problem for insurers, and one of the major benefits of AI is expected to be new ways to help address it. This series of blog posts focuses on the use of analytics to detect and prevent auto insurance fraud. The articles in the series provide a structured approach to the problem, describing various analytic techniques and measures that can be used to detect and prevent fraud. Issues discussed include data management and data quality, the use of business rules and watch lists, and social network analysis.

Insurance companies have a long tradition in the use of analytical methods. Where does the insurance industry stand in terms of modern analytics and artificial intelligence? This is what our industry experts found out in personal talks with insurers from all over Europe. Read the survey results.

 

Digitisation vs digitalisation

Will AI and advanced analytics be used for digitisation, digitalisation, or both in the insurance industry? Digitisation is the (fairly basic) process of automating processes, including customer-facing ones, and delivering them digitally. It is a major driver of efficiency in many industries. Many of the processes in the insurance industry have not greatly changed over the last 20 years, and there is therefore a sense that digitisation is long overdue. This article by Michael Rabin discusses the digitisation experience of one big insurance company in Europe, and explores some lessons for others starting down this path.

Digitalisation, on the other hand, is the complete transformation of an organisation using digital techniques. Digitalisation is gaining ground in insurance because of the understanding that customer data, as a means to improving and tailoring experience, is increasingly important. This article discusses some of the drivers of digitalisation, including changing rules of competition and the arrival of new competitors, the rise of new distribution channels, and the implementation of multi-channel management.

I hope you find some of these useful – would love to hear what you think?