The Italian insurance market, which has about 200 registered companies with a turnover of over 160 billion euros, is undergoing a process of transformation mainly linked to new consumer behaviour, the introduction of new technologies and the entry of players from other sectors. Companies are trying to compete in this highly competitive scenario by adopting various strategies: process innovation, the study of new products, technological partnerships, the investment of internal IT skills. To maintain profitability in a market that presses down on prices, companies must focus on their knowledge of their customers and their ability to predict their behaviour to offer products that are increasingly tailored to consumers. In fact, over the last decade, major insurance companies have invested heavily in automating the process of capturing data about their customers and their buying habits and behaviours. However, it is only in the more recent past that they have started to think about how to transform this data into useful indications for optimizing the offer and improving commercial performance.
Enhance risk assessment methods with AI
The key to the success of any insurance company lies in the correct assessment of the risks covered during the underwriting phase and the consequent definition of the offer. In the presence of large amounts of data, such as those held by insurance companies, with the support of AI tools it is possible to obtain a more effective and customized risk assessment, thanks to the identification of the best predictive model obtainable from different types of sources.In contrast to what emerged in the first phase of experimentation of the application of artificial intelligence in this area, AI has the potential to strengthen current valuation methods by using methods characterized by complete traceability of the factors taken into account in the assessment and of the outputs that can be obtained and therefore in full compliance with the regulatory constraints imposed, replacing the more controversial “black box” methods.
Supporting acquisition and retention strategies
The use of AI techniques applied to Insurance has demonstrated its effectiveness not only in the calculation of risks but also in other areas, such as Sales & Marketing activities, both from the point of view of Customer Acquisition and Churn prediction and reduction. Starting from the first area, being able to predict whether there will be a conversion following a request for a policy subscription quote is a complex process, but AI allows you to play in advance by optimizing marketing investments by identifying users who, due to previous purchases, master data and behaviours, are more likely to convert. The high number of factors impacting policy choices and a large amount of data available make the insurance environment perfect for maximizing the potential of AI. In this case, for example, through correlation and clustering, it is possible to identify the factors most influential on purchase choices in people who have already taken out a policy and thus identify individuals with similar characteristics among potential customers. Through the use of recommendation engines, it is also possible to generate targeted offers that allow the distribution network to offer services that appear to be created ad hoc for the individual user, thus increasing the probability of conversion of the quote into a subscription.
Insurance Case History ENG
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Artificial Intelligence tools offer great support to direct the actions of insurance companies in a data-driven way even in the customer management process. Considering that the cost of acquiring a new customer is greater than the effort that the company sustains to keep an already active customer on board, being able to obtain accurate analysis also in terms of Churn Rate has a positive ROI compared to the policies currently adopted by most companies.
Thanks to the most advanced techniques of Artificial Intelligence, it is possible to associate to each customer the relative probability of abundance in a determined period. Therefore, being able to model a valuation method and clustering customers based on their relative importance and probability of abandonment is a challenge that all companies should face to improve their competitiveness and increase margins.
These are just some of the areas where the strategic role of AI in the insurance industry is evident. It should not be forgotten that the fundamental aspect on which all the practical applications considered rest is the ability of Artificial Intelligence to transform the data collected into useful information to guide the strategies and specific commercial actions of companies. Therefore, only with full awareness of this, future projects will be able to effectively follow the path of innovation.