Gras Savoye – Insurance Markets 2018

marchés de l'assurance 2018

The broker Gras Savoye, belonging to the Willis Towers Watson Group, published in september 2017 an interesting economic note on the transformation of the insurance markets 2018. They come back therefore, by business line, on current trends.

Overview

Overall, the French insurance market is rather stable:

  • The world market is down slightly
  • France remains an important market (5th world player), with just over 200 billion euros of premiums
  • Personal insurance accounts for 75% of this figure, and sees a rise in pension premiums
  • Property insurance is subject to strong competition, and is experiencing a significant increase in claims, which affects the underwriting results. This is due in particular to climatic events.

In the details of the 2018 insurance markets

In brief

Given the richness of the report, it is complicated to synthesize it, and I prefer to suggest that you refer to it if necessary. I prefer to present the plan!

insurance markets 2018 insurance markets 2018

Each theme is treated according to 5 axes:

  • Capacity / Appetite
  • Price
  • Deductibles
  • Warranties
  • Flexibility

Some key things to remember

  • 3 alternatives to risk transfer are presented:
    • Structured Insurance: covering the multi-year consequences of new or complex risks. (terrorism, pandemics, strikes, etc.)
    • parametric insurance : or index-based insurance, which i have already talked a lot here
    • Mutli-line / Multi-year approach: which reduces volatility
  • D&O (Executive) policies continue to evolve and adapt to changing risks. The tailor-made is put on this profitable and dynamic market (new regulations, cyber risks, etc. which increase the risks for managers).
  • There is growing interest in combined fraud / cyber collateral, although these mounts do not always yield the best proposals. Note: It is understandable that customers are always looking to hedge against an unknown risk, but perceived as critical .
  • Kidnap & Ransom products are in full swing, because of an international geopolitical context that is sometimes hazardous.

Conclusion: My opinion

This report is a mine of information, very technical, but essential to a good understanding of the complexity of heavy risks. A disadvantage on the bedside table, but imperatively under the elbow!

In hollow, one sees there appear, as often, the insurance products which could be generalized tomorrow. The trends observed, especially on cyber, fraud or regulatory aspects are in this sense good indicators of the future.

Lexis Nexis – Best practices for predictive modelling

best practices for predictive modelling

Version française ici


Lexis Nexis published this summer a white paper on the best practices for predictive modelling , or more precisely on the steps to follow to implement this type of solutions for small commercial.

According to them, 4 steps are necessary for a product creation of this type:

  • Ideation
  • Design and development
  • Implementation
  • Monitoring

So far nothing transcendent, isn’t it? Let’s check it out!

Ideation

Successful ideation assumes that two conditions are met: a strong sponsorship and a cross-functional team.

The responsibilities of this team are as follows:

  • Identify and validate the business problems to solve
  • Generate ideas on how to solve these issues with predictive models
  • Select the best ideas
  • Highlight the benefits of predictive models
  • Calculate implementation costs
  • Determine the ROI and justify the use of predictive models in relation to another solution
  • Establish acceptance of the topic among the teams.

Design and development

The report is focused on contracts / products for small businesses. The suggestion is then to go through an “insurance score” to analyze and estimate the risk and to price it, according to a probability of losses.

3 steps are needed:

best practices for predictive modelling

  • Data mining : nature of data, sources, refresh frequency, etc. For example, it is possible to use historical loss experience data ( Note: obviously …! ), but also credit data, or public data about the company. In a more detailed way, the geographical location is relevant. ( Note: at this point, note that we do not use anything complex! )
  • Model creation and validation : This is to determine, on the basis of a set of data, patterns or correlations that recur. We are here in deductive mode, we start from data to deduce a model. The challenge is to identify which data plays a role in achieving the desired goal . Then, it is possible to test the identifier models on data and production processes, to ensure that, when capturing the data, it is possible to categorize a new customer using the defined models.
    • Here is the kind of report that can be generated to define a number of groups to automate the subscription with 3 possible actions: acceptance (right), visa application (center) or automatic refusal (for worst groups).

    best practices for predictive modelling

  • Regulatory review : This aspect specific to the American market (but finally quite close to the regulatory aspects valid everywhere), suggests to compare the required data with the specificities of each state, and to apply, where applicable, restrictions.

implementation

The implementation is based on a few key steps

good practice predictive models

Monitoring

Finally, monitoring the relevance of the model assumes, on the one hand, to track the use that is made of this product, but also to measure its effectiveness.

best practices for predictive modelling

Monitoring

Finally, monitoring the relevance of the model assumes, on the one hand, to track the use that is made of this product, but also to measure its effectiveness.

best practices for predictive modelling

Tracking : Scores must be tracked when applied and when modified before application. In these latter cases, it is important to understand why and possibly modify the model iteratively to improve it.

Efficiency : The most important thing is to make sure in the long run that the model is good for achieving the business objectives that were originally defined. If this is not the case, it must either recalibrate (keep the mechanisms, but readjust the valuations), or rebuild it!

Small bonus on best practices for predictive modelling

Moreover, on this subject and always by Lexis Nexis, I invite you to consult this video, which includes some of the fundamentals:

best practices for predictive modelling

Selfie-Insurance: underwriting on physical appearance

assurance au selfie

En Français ici


A recent communication from Quilt, the us-based company offering an insurance underwriting based upon a selfie, has been massively shared (especifically in France on this very good blog). More shared anyway than this article published in june about Lapetus, the underlying technology! It is the opportunity to come back to the operating model, the advantages and the drawbacks to keep in mind. Continue reading “Selfie-Insurance: underwriting on physical appearance”