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 In recent years, insurers have implemented transformation projects to gain competitiveness. The Combined Ratio of the property and casualty market gained one point from 99% before reinsurance in 2014 to 98% in 2018 (source FFA). However, the economic equation for the damage branch was mainly kept positive thanks to financial products. The financial balance went from 7.2% to 6.1% over the same period. With negative interest rate levels, non-life insurers are once again challenged on their ability to generate margins.

Optimization of the sales journey

The digitization of the sales path remains a major axis of value creation for the players. Beyond improving the customer experience, the digitalization of the journey provides real sales time savings thanks to a more exhaustive customer knowledge that avoids the need to remind you of missing information or re-enter information during the process. subscription. For example, technology and the increase in available data allow insurers to considerably reduce the number of questions asked of prospects by simply knowing the home address or the vehicle’s license plate.

Sales support solutions have also evolved well, integrating Machine Learning models capable of identifying the products suitable for prospects with the best probability of realization. AI solutions integrate the functionality of voice recognition to try to improve its use with salespeople who no longer need to enter information. Like Zelros, which has integrated this feature to assist advisors in real time with the objective of improving the rate of transformation and multi-equipment. However, we see the deployment of Machine Learning solution still very timid in the physical distribution networks especially due to the fact that the AI ​​solution has often been inserted into the existing paths without having rethought the customer-advisor-AI sales path ?

Reduction of management fees

With a weight of 8.5% of gross contributions, claims management costs also remain a pocket of major potential gain for players. The implementation of a cost reduction plan in recent years has resulted in the automation of certain repetitive tasks via basic RPA solutions, which has nonetheless made it possible to generate operational performance. The use of machine learning model combined with RPA solutions is now becoming essential to go beyond simple automation. Natural language analysis models have progressed enormously and can now make it possible to automate certain complex management acts from end to end.

The first step in the digitalization of processes has now enabled process mining technologies to become more democratic. In fact, the availability of data in management makes it possible to model end-to-end claims processing. These solutions allow a better knowledge of the reality of processing time and more personalized and targeted corrective measures. Thanks to these solutions, players can predict that certain claims will cost more in management costs than compensation for the claim.

The use of self-care

The total delegation of certain actions to policyholders is also an underutilized lever for optimization. Indeed, several players who have developed their self-care observe that the rate of use is limited, in particular due to the complexity of the insurance universe, but also to the often still perfectible relevance of assistance via chatbot or callback. Many policyholders who have started an online request drop out and proceed directly with a manager. These dropouts can be generated by an unsuitable self-care course with a multiplication of the solutions offered: search engine, dynamic FAQ, chatbot, etc. but also by the lack of a satisfactory answer in the eyes of policyholders. Improving self-care involves analyzing these dropouts and continuously updating responses. This continuous optimization can take different forms such as the analysis of responses having obtained a score below a satisfaction threshold, a volume of search by keywords which obtained no results, etc.

All these self-care, process mining, RPA and Machine Learning technologies are only complementary tools and their use must be adapted to the desired objectiveThe approach must be to think in its entirety turn on the problem to be solved which will optimize the use and the ROI of the implementation.

Rhalid Bouakhris, Senior Consultant of the Financial Institutions division

RHALID BOUAKHRIS

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