Avantages is an insurance brokerage and management company that operates as a wholesale-broker. Avantages designs and manages affinity contracts that are marketed by a network of 750 distributors.
E.Leclerc, a major French retailer, provides their customers with a light vehicle (LV) or commercial vehicle (LCV) rental service that includes compulsory car insurance, given the short duration of the rentals. In order to limit the commercial impact of a high cost of insurance, a significant deductible is set up (1200 €). This is why E.Leclerc centres wish to offer their customers a partial deductible buy-back contract as an option. This allows E.Leclerc to offer a more premium service to their customers, without increasing the basic cost of the insurance, and also to improve their margins.
To set up this deductible buy-back program, Avantages asked Seyna to assist them. The product is a classic partial deductible buy-back contract, allowing the final customer to reduce his deductible from 1200€ to 300€.
Partner's website: http://www.avantages.mc/fr
The classic structure of the deductible buy-back product did not allow Avantages to implement it easily. Indeed, a classic product would have required a significant amount of specific IT development for Avantages. The available fields in Avantages’ management system did not allow them to integrate all the data variables of such a product.
Avantages therefore asked Seyna to structure the product differently so that it could be integrated to the existing management systems by reducing the number of variables to be implemented, while also answering the needs of their client (E.Leclerc).
On top of this, Avantages required the implementation of the program in a very short time, putting to the test the speed of Seyna teams and technology!
It was therefore necessary to review the classic structure of the a deductible buy-back contract.
To answer this challenge and to design an innovative premium structure, Seyna's Actuarial and Data Science teams carried out in-depth analysis on the loss ratio of this type of product.
This allowed us to discover a plateau effect of the loss ratio from the 15th day of a rental, which allowed us to propose an efficient and innovative structure, even though is sounds counter intuitive:
Indeed, the number of claims is not linearly proportional to the number of rental days. Mechanically, the probability of a claim occurring is proportional to the number of days the vehicle is “really" used and not the days rented. However, while vehicles rented for very short periods are used every day, those rented for longer periods (> 15 days) have an actual usage rate of less than 100%. Thus, the number of claims varies very little whether the vehicle is rented for 16, 20 or 27 days.
Seyna's unique ability to have a very quick visibility on the technical performance of its products, allows us to propose this kind of innovative pricing structures..
Seyna was able to absorb the complexity of the problem raised by its partner by approaching it from a new angle, and thus propose an insurance solution to an operational/IT problem to simplify the management on the partner's side.
The program has been a strong success with a significant volume of premiums generated.
It also gave Avantages a turnkey offer, which was easy to implement, which Avantages can now deploy to other distributors.
Seyna is a partner who stands out through its reactivity, its customer centricity, and its capacity to decipher market dynamics; a partner with whom the APRIL teams enjoy working.
Seyna and Lite were able to merge their analytical and insurance expertise, in order to guarantee that those who commit to better consumption will be rewarded for it.
No less than 20 000 prices were generated, giving us the highest level of product customisation on the market.
Compensation in unrivalled speed - thanks to an integration in Seyna's systems directly via API.
Being backed by a partner such as Seyna had been critical for the success of our program. We especially appreciated their ability to bring innovative pricing models and adapt to suit our needs.
