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Predictive analysis: differentiating factor in the insurance sector

by msg life / 17. April 2020

One of the most prominent themes in countless media, studies and reports – digital transformation – represents an extraordinary opportunity for the insurance sector. Specifically, advanced data processing through predictive analysis has translated, for life insurers who are investing in this technology, into tangible and measurable returns.

The return on investment (ROI) in these technologies is necessarily positive because the predictive analysis allows to anticipate events, through the application of data and techniques to obtain information, in order to manage the insurance business in an optimized way, significantly improving the customer experience.

The reality is that, while it is true that there are many companies that store large amounts of data, they are of little use if they do not extract the real value of the data obtained. This position is reinforced by the most recent report published by Willis Tower Watson, Predictive Analytics Speed Innovation for Life Insurers (2019), which reflects the results of a survey of fifty-one life insurers in the USA, which add up to a higher annual turnover to 138 billion dollars. According to the analysis of this global consultant, the market is currently at a turning point in terms of the development and use of data analysis to transform the insurance business, especially in the life sector.

Furthermore, as we have already mentioned on other occasions at msg life, there is no doubt that the insurance sector has been under increasing competitive pressure, as well as changes in customer expectations, which are increasingly demanding and with quick access to a large number of sources, which, on one hand, allows them to compare the existing offer and, on the other hand, it encourages insurers to have greater pressure to have a competitive offer.

In the specific case of life insurers, and according to Willis Tower Watson, these pressures and changes have increased the growth of their bet on predictive analysis, investing more and more in cutting edge technologies. This is confirmed by the fact that 100% of the medium and large insurance companies surveyed by Willis Tower Watson claim that, within a maximum period of two years, they will have accelerated their innovation plans through predictive analysis, in the life insurance Group, and doubling the current percentage in the case of individual health.

On the other hand, more than two thirds of the interviewees classified the need to strengthen the relationship with the customer as an “area of great importance”, materialized in making the offer available to the customer as soon as possible, personalizing the experience as much as possible and facilitating interactivity. To achieve these goals, insurers will need the most accurate predictive data analysis possible, and in this sense, it is worth noting the recent introduction of figures from the data analyst or scientist into the team of many insurers.

These experts agree on pointing out the incremental importance that wearables are also acquiring. The widespread use of electronic devices (smartphones, smart bracelets or necklaces, etc.) allows, in addition to encouraging healthier lifestyles, well-being and prevention, to monitor customer data and adjust, based on the information obtained, the characteristics your health or life policy. Based on the American reality, with less restrictions on the use of data by insurers, it is estimated that the forecast of its use among life insurers will rise from the current 5% to 42%, within a period of five years.

In operational terms, predictive analysis is nurtured by artificial intelligence (AI) and machine learning (ML), and its benefits do not end with what has been described so far – a real predictive model allows to locate and anticipate almost ten times more customers at risk of escape, improve complaints handling and fraud detection and prevention.

As if all these data were not enough to guarantee the importance of predictive analysis, the return on investment reinforces the added value of investing in these technologies – more than two thirds of the life insurers surveyed by Willis Tower Watson claim that, thanks to them, managed to reduce the issue / subscription costs and 60% attributed the growth of their sales and profit to the use of predictive analysis.

Finally, it is important to emphasize the importance of choosing an appropriate technology or analytical technique – that allows choosing and accessing the most valuable data and, above all, that best suits the specific objective sought. In any case, there is no doubt that maintaining legacy systems has been a major obstacle to progress.

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