NEWS – What you may have missed in 2017

revue de presse du 17 avril 2018

What you may have missed, like me in 2017!

If like me, you follow between 50 and 70 sources of information (rss feed) on a daily basis, not counting the linkedin and Twitter publications, it sometimes happens to lose your mind and miss some interesting information.

More specifically, it often happens to see an article that we think would be interesting and keep it for later. But this later comes only rarely and we end up with a small stack of articles to read!

For me, this blog has solved the question about the publication of reports, which remained on my desk waiting for something. For the articles, it is not quite that still, and I find myself at the end of 2017, with a good fifty articles under the elbow. Here are some of them, which you may have missed too!

The Tribune – Assurtech: a disrupted industry, for the benefit of the customer?

link: https://www.latribune.fr/opinions/tribunes/assurtech-une-industrie-disruptee-au-benefice-du-client-717687.html

Daniel Haguet, professor of finance at EDHEC, who was few (hum) years ago my teacher, gives his opinion on the phenomenon:

  • 75% of insurers consider that their industry could be disrupted
  • Big data, by aggregating data, even out of the insurance, allows a customization of offers
  • Distribution is growing online thanks to the rise of the internet
  • For many players, the challenge is to take a stake in these structures, to take advantage of both technological advances and the “captive” portfolios of clients.

Note: For a selection of insurers’ strategies with insurtechs, it is here !

Structuring technologies and forces

Seen on Twitter, but unfortunately without noting the source, I share it, because although incomplete without a caption, it seems to me wider in its acceptance of technologies than many current reports and panoramas, including mine !

what you may have missed

Here’s another one, which gives a little overview of the issues ahead.

what you may have missed

Medium – Insurance as a smart contract

link: https://medium.com/aigang-network/insurance-policy-as-a-smart-contract-fully-automated-process-too-good-to-be-true-39c613c18d8d

Aigang Network summarizes what insurance contracts will be tomorrow, thanks to smart contracts. Better yet, rather than a long speech, they made a demo, wrote the code around an Ethereum blockchain ( accessible on github ) and an app to test it, as well as a video, here:

Note: For a selection of articles on the blockchain, this is here

Risk management – Investing in the insurtech toolbox

link: http://www.rmmagazine.com/2017/06/01/investing-in-the-insurtech-toolbox/

A small inventory of insurtech solutions that address the issues of risk management:

  • Understory weather
  • Safety culture
  • Security Scorecard
  • Risk IQ
  • CapeAnalytics
  • Cyence
  • Human Condition Safety
  • DAQRI Smart Helmet

Note: To find my overview of insurtechs, it’s here !

Intercom Blog – Machine learning is easier than it looks

link: https://blog.intercom.com/machine-learning-way-easier-than-it-looks/?utm_content=buffer25695&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
Once is not customary, a somewhat technical article, but that gives a specific example of what can be done with machine learning in 40 lines of code. Frankly, it’s a gift!

Note: For a selection of articles on artificial intelligences, it’s here !

The digital journal – connected objects of health, french social security does not perceive the added value

link: http://www.larevuedudigital.com/objets-connectes-de-sante-la-securite-sociale-nen-percoit-pas-la-valeur-ajoutee/

The title is deliberately provocative and surely a little removed from reality, when we know the current reflections! However, Laure Beyala, a biomedical engineer in Bichat, who has published a book on connected health objects, offers an interesting vision of the issue. She returns in particular on key concepts: Role of the social security, the insurers, DMP, brakes with the development, etc.

Note: For a selection of articles on connected objects, it’s here !

P & C360 – Telematics in auto claims is inevitable

link: http://www.propertycasualty360.com/2016/08/18/telematics-in-auto-claims-is-inevitable?slreturn=1515504588

This article is totally in sync with my opinion on embedded telematics (see my predictions for 2018 or even my note of conjuncture on this subject ). It goes back to what can be done with it, and why the use of this technology for disasters is inevitable!

Note: For a selection of telematics articles, it’s here

Insurance Thought Leadership – Why is Customer Experience Key?

link: http://insurancethoughtleadership.com/why-customer-experience-is-key/

This article is about a concept that I consider essential! Data, processes, technologies must be oriented around the customer and his needs. This is all the more true as connected objects and their billions of data become more and more accessible in the years to come.

Note: For a selection of articles on the user experience, it is here !

Design thinking

A visual that I like about design thinking and ideation strategy that I integrate more and more in my thoughts because totally related to the notion of innovation. The content could not be done without the form …

what you may have missed

Innovation Insurance – My 6 topics for 2018

6 topics for 2018

As it is frequent at the beginning of the year, I too will fall into the game of predictions for the coming year. With my 6 topics for 2018, here are the 6 points that I think will be 2018 on innovation in insurance:

IOT

I think the moment is good to launch complete offers around telematics embedded in cars, smart homes or wearables. When I think of complete offers, it does not just mean capturing user data. It is also, above all, and before all, providing services to these policyholders or using these tools to implement new prevention policies.

Redesigning offers

This may be the consequence of the previous point, but a little wider, a redesign of offers. It’s time to think about offering insurance products as the structuring element of the insurance value chain, from prevention, to disaster, to adding service. For example, the improvement of claims management will only pass a complementary course when products are designed to be managed quickly and simply.

Platforms

I often talk here about decomposing the value chain with the setting up of experts at all levels, namely, risk bearers, distributors and managers, for the purely insurance part, but also service providers. ecosystems that will need to be mobilized intelligently. The advent of the platforms will aim to move towards this type of organization where everyone will find his place!

API

As a direct consequence of the previous point, if the same contract must concern multiple interlocutors, the notion of interface between them is essential. Maturity on these issues has greatly increased in recent years. Simple EDI flows (like those still used for third-party payment for example), we moved to advanced webservices but sometimes complicated to set up. Then, we now come to expose real complete services, through webservices or APIs to access the data without necessarily changing the technical architectures in a structuring way.

Insurance “cyber”

The needs in this segment are exploding, and it will be an intelligent response that avoids the pitfall of systemic risk! For me, here more than elsewhere, it means helping to prevent and reduce risks upstream to avoid the most frequent attacks. On the technological side, the weakest link is always first and foremost the human, it is about takins this into consideration! By the way, how do you manage your passwords?

A user experience at the heart of all steps

The last few years have been mostly devoted (except for the more advanced ones) to work around the efficiency of processes, and how to do it for less! It is now to be interested in doing better for the same price, or better for less expensive. Better means here better from the point of view of the customer, because the essential thing is to simplify his life, to eliminate irritants. It is therefore customer demand that must be at the center of future optimizations.

It goes without saying that Siltéa and I are able to accompany you on these 6 topics for 2018! Feel free to contact me .

And you? What do you think? Do you have others?

Insurance innovators – Future of insurance 2017

future of insurance 2017

Version française ici.


Insurance Innovators (an offshoot of Market Force, which I had already talked about here for their report under the same title ) has just published the future of insurance 2017 (Future of General Insurance Report 2017).

7 themes are discussed (for a better readability I separated into several pages):

  • Innovation and Disruption
  • A changing regulatory environment
  • The future of underwriting and pricing
  • Value-added services
  • Touch Millenial Generation
  • Insurance in the Age of Machine Intelligence
  • Fraud in a connected world.

Written in partnership with the Chartered Insurance Institute , and sponsored by IBM, Sas and Smart Communications , this report is generic, but of quality. Market Force believes that insurers are poised to innovate and transform to keep pace with insurance companies, but the pace of transformation is still too slow. A tip: Act now! Continue reading “Insurance innovators – Future of insurance 2017”

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