Machine learning in Insurance
Insurance has been a field that traditionally relies on mathematical and statistical models to assess risk and risk premiums. More so than perhaps any other traditional industry, insurance has been using quantitative tools to measure and manage risk, and make sure the insurance companies are stable and turning a profit. Since data is fundamental to this quite well developed sector of the economy, we observe a number of mathematical and statistical models in use, sometimes quite complex in nature. They are necessary for risk estimations and calculations, which may become quite complex quite fast. The question then comes naturally – can machine learning and AI bring something more to the table, in addition to what has already been done? Or perhaps it can offer just marginal improvements over the existing models? We believe the ML can offer much more than just incremental improvements, and let me explain why that is.
While insurance mathematics has been around for a long time, machine learning adds techniques that can build much more sophisticated models, focused on elements which were not the focus of the traditional approach. Methods like decision trees, support vector machine, random forest, K-nearest neighbour, neural networks, etc., give not only additional angles of the analysis but also opportunities to approach business differently. These techniques do not necessarily have to focus only on better calculation of risk – neural networks for example can be used for both direct data modeling and also operational tasks: development of marketing campaigns, image and voice recognition, churn prevention, customer segmentation, fraud detection, etc.
Talking about risk modeling though it is good noting that the new techniques do affect substantially the way we look at risk. Our experience with ML models is that typically that the more data you have and the more individualized your observations are, the better ML works. In fact some of the methods – neural nets for example, really thrive when the amount of data is very substantial, in such cases their performance can be significantly better than most traditional approaches. Another key difference lies in the fundamental focus on segmentation and conventionality reduction, which brings the focus to the individual, rather than the general population. In that regard, at least three key ML opportunities for the insurance industry are likely to become important in the following years:
- Personalized insurance. Using various machine learning techniques, it is possible to do an ever-finer circle around your customer and tailor service more than typically considered possible. In addition to the much richer methods to achieve that, ML provides option to look into other data and combine data sources typically disregarded in such analyses. That could allow customer profiling/segmentation by combining all available data to assess better risk but also to decide on what products that person might be interested in or what products might be better suited for his or her lifestyle. While product design is a complex topic, employing ML techniques may bring it to a new level, allowing for more personalized insurance products and more personalized offers, improving customer satisfaction and lowering churn rates.
- Segmentation. Analyzing your data invariably involves making segmentation. If you have two distinctly different populations which, you cannot distinguish currently, you might be tempted to analyze them only on a combined basis clearly skewing results and increasing the modeling risk. Importantly, looking at the market as one segment instead of multiple sub-segments may have the same effect. For example you may look at car insurance as one population in terms of sales opportunities but ML analysis may identify more diversity, with several groups with potentially very different characteristics, including risk profile. The right customer segmentation is key both for your risk management and optimal customer acquisition, given your risk appetite.
- Scoring. Machine learning techniques are especially suitable for scoring (e.g. of applications) based on historical data available. Sophisticated models can be created based on ML algorithms that extract key and important features in applications to estimate a score, useful in a variety of ways – in application approvals, insurance pricing, insurance monitoring and claim management. ML created scorecards can follow the lifecycle of the product sold as well as the life of the user as a customer of the insurance company to make sure not only risk is properly priced but that the best possible products are offered to the current and potential customers.
There are a number of other fields where ML is making breakthroughs, including fraud management, claim automation, document automation, personalized assistants, etc. That is why we believe ML will impact and improve the insurance industry substantially in the years to come.