Press review January 15th, 2018

revue de presse du 15 janvier 2018

Here is a little overview of articles that caught my attention and deserve to appear in the “press review January 15th, 2018”!

O’Reilly offers books on data and artificial intelligence

The famous publisher of computer science books has a library of 80 of its publications on big data, artificial intelligence and data science, published between 2012 and 2016. Essential detail: the download is free, in pdf, epub or mobi.

Remember: when it’s free, you’re the product! O’reilly asks for your name, first name and email to download, but nothing forces you to enter the real …

press review January 15th, 2018

Not so accidental road accidents

Mathieu Grossetête is a researcher at the university research center on public action and politics. We propose in the Diplomatic World a reading interestingly, accidentology by car.

It appears that the prevention policies of Road Safety may now reach their limits because they are poorly targeted! Or when the art of data analysis becomes essential to better control a phenomenon, and treat it correctly!

Exciting article to read, which makes us rethink the notion of risk analysis, the perception of events. Finally, it simply invites us to change our way of thinking.

press review January 15th, 2018

Typologies of wearables

Evan Kirstel, one of the leading thought leaders in connected health, offers us a panorama of typologies of connected objects for health, aka wearables. This one goes a little further than the usual dichotomy watch and bracelet vs balance!

press review January 15th, 2018

Deloitte: human collaboration – machines

A visual of a Deloitte study, found on Twitter, gives an interesting reading of the symbiotic relationship that can develop between humans and machines. All thanks to the service rendered!

press review January 15th, 2018

Axa and telemedicine

Jacques de Peretti, CeO of Axa in France announced this week the launch of telemedicine in companies and openness from service to experts.

The second service is the extension of the current service (which I had the opportunity to test personally and which is very good). On the other hand, the first one supposes major investments and questions around the model chosen by Axa.

The question of danger, posed by Olivier Harmant, is in my opinion not relevant.

press review January 15th, 2018

However, what is the return on investment of this model for a complementary insurer? Indeed, a teleconsultation at the expense of the complementary supposes a cost support higher than the usual part. In order to be financially attractive, such a service needs to be either cheaper than normal consultation or to reduce risks. For now, teleconsultation is a vector of brand image and carries a demarcation line with respect to the competition. However, when everyone will do it, it will be necessary to consider the business model that goes with it!

Finally, should not this be the role of an intermediary platform? The latter would make this service available and look for the insurer who answers it. On the contrary here, it is carried directly by a complementary insurance? What does this say about our public health insurance? A lot of questions, few answers for now!

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

VA²CS – Detection and prediction of falls

Detection and prediction of falls

Creative Specific Software has developed an innovative solution for the detection and prediction of falls for the elderly. This solution is called VA²CS.

Halfway between connected object and artificial intelligence, it allows to offer a real service of prevention of a significant risk! <! – more ->

The need

For the record, there are in France 1250 falls per day, for 30 deaths. The need to detect the fall is therefore not new, and several solutions already exist. However, most existing solutions rely on bracelets, pendants or watches, and this, without major innovation for 25 years. The latest solutions are based on ground-based sensors and teleassistance boxes ( see the Harmonie Mutuelle solution – Orange ), but the cost remains generally important.

There are several major limitations to these solutions:

  • The first reaction of a falling person is not to press a button, whatever it is, but to try to get up
  • Once standing, the 2nd reaction is not to try to call for help because they do not want to disturb. The person is not going to be taken under examination immediately, with all the consequences on health aggravations in particular.
  • In the event of a fall with loss of consciousness, not all solutions can automatically send help
  • Most calls via teleassistance boxes are calls of convenience (against loneliness, need to talk to someone, need for psychological assistance, etc.). The average duration of these calls is almost 3 minutes.

Moreover, in the event of a trigger or an appeal, the tele-assistants have the legal obligation today to warn the helpers (under risk of not helping a person in danger). When the family is not available, this generates firefighters’ intervention costs (180 €). When we add the potential damage to the home, when it is not always relevant, because there is not always a fall, the consequences are heavy.

The solution

Fall Detection

With this in mind, and after working with Dr. Jean-Marie Vetel, one of the designers of the french GIR grid , Ramzi Larbi and his team have developed a new solution.

This involves equipping the living area with sensors (cameras), and then, thanks to the analysis of images captured in real time to detect falls.

When it is detected, a photo is sent automatically to the remote assistance center. It takes on average about ten seconds to give a certain answer to the action to take:

  • the fall is real and you have to send help,
  • either there is no fall, it is a false alarm, and the alert can be closed.

The desire is to obtain a solution at a reasonable price, the offer is based on conventional cameras and a connected box that contains intelligence and algorithms.

VA²CS already has 1500 EHPAD rooms in France, within the largest networks (Korian / Orpéa), as well as 600 private individuals. A partnership also exists abroad with Tunstall, the world leader in teleassistance for the elderly. The algorithms are patented in France and North America.

Prediction of falling

In order to complete its offer, the company has worked in partnership with Orpea to identify key factors in prediction of a fall. Then, she developed the corresponding algorithms. It is now able to predict a fall with a precision of about 60%!

Implemented in testing at Orpea, this solution has reduced falls by 50%!

Extensions

The solution was then supplemented more conventionally by intrusion detection coverage or facial recognition of visits to notify malicious visitors to relatives.

My opinion on detection and prediction of falls

This solution was not developed at first with a desire to meet the needs of insurers. However, it is totally in line with the current concerns of the sector.

The potential consequences of this type of solution for public health and for insurers are in my opinion significant.

Indeed, being able to predict a fall makes it possible to send notifications to qualified personnel or a relative to intervene. This is obviously valid in specialized institutions (EHPAD) to support staff reduced at night, but use cases can go further.

Thank you Ramzi Larbi for this exchange and good luck!

Note: I do not have a commercial relationship with the company providing this service.