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Emerton Data positions itself as a high-end consulting partner to its clients, and thus must be at the forefront and ahead of the curve on many data & AI transformation topics
Play icon2022 AI for Industry
by
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Aimé Lachapelle
Aimé Lachapelle
and
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Ugo Philippart
Ugo Philippart
and
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Mathilde Binet
Mathilde Binet
September 2022

An ever-increasing digitalization of industrial activities, ever more voluminous data generated, a potential of business value still under the radar of most decision-makers,

Nothing new about AI, you’d think.

Well, think again!

Play iconDeep learning in insurance
by
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Aimé Lachapelle
Aimé Lachapelle
February 2020

If deep learning will definitely play a crucial role in automation and improvement of the daily insurer operations involving image, text, and audio processing, it is not expected in the mid-term to deeply impact the core insurer business related to risk management.

The aim of this short paper is to share a point of view on the use of deep learning technologies in the insurance sector.

Play iconData vizualisation on confinement impacts on mobility
by
and
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Aimé Lachapelle
Aimé Lachapelle
and
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Yannick Léo
Yannick Léo
May 2020

The unique analysis of data from more than 30 billion geolocalized steps, carried out jointly by Emerton Data and WeWard, sheds light on the various mobility phenomena observed since the announcement of confinement in France.

The results presented in this report provide food for thought on the one hand on the implications of a generalized and uniform confinement across the country, and on the other hand on the methods of implementing deconfinement or a revised confinement. The announcement of confinement, made a few days before its effective date, provoked many long-distance pre-confinement trips: around 20% of French people changed departments, mainly in the departments adjoining their place of residence.

To what extent does this massive departure for the 18-45 age group concern family reunification with parents who are vulnerable to covid-19?

Play iconAI for Industry
by
and
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Aimé Lachapelle
Aimé Lachapelle
and
,
Yannick Léo
Yannick Léo
and
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Haithem Choukatli
Haithem Choukatli
November 2020

Update on the challenges and opportunities of an industrial world renewed by AI

Play iconAI for manufacturing
by
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Aimé Lachapelle
Aimé Lachapelle
June 2019

It is time to acknowledge the challenges to delivering significant impact from the use of AI in manufacturing industries.

One of the main causes derives from a prevalent mistake: AI & data tools have not been effectively conceived, neither marketed, as services for operators in factories, resulting in poor adoption and in worst cases in mistrust behaviors. In factories, humans will be playing a core role for a while, and their “customer” needs and “customer” experience have to be carefully addressed and marketed in the plant when designing AI & data tools. This is a key success factor to massive adoption of AI solutions in the coming years.This short paper is based on Emerton Data research and analysis, and on numerous interviews with industry experts and solutions providers.

It provides an overview of promising AI use cases and solutions for manufacturing, and detailed hints to unlock the value potential.

Play iconModel Risk in the Age of Artificial Intelligence and Machine Learning
by
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Aimé Lachapelle
Aimé Lachapelle
and
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Ali M'Rabeth
Ali M'Rabeth
October 2019

An increasing reliance on Artificial Intelligence for decision making is driving financial institutions, regulators, and supervisors towards a clarification of sources and control of risks. These risks were either already present (but marginal) or even non-existent in the usual model risk management framework. In a context where the use of machine learning is becoming massive and industrialized across banks and insurance companies, problematics such as interpretability and dynamic monitoring, robustness, ethics, bias and fairness require a specific attention.

Although all these topics are becoming active academic research topics and business innovation fields, their rigorous analysis from the model risk point of view remains at its early stage. A close collaboration between academics, regulator experts and private sector professionals can accelerate finding pragmatic answers to multiple important questions, e.g. how to interpret outputs of black-box models? How to monitor machine learning models in time? When and why do they deviate? How to control the discrimination incurred by the algorithms? How to prevent the effects on decisions of input data changes or data falsification?

This short paper is based on Emerton Data research and analysis and provides an introduction to the newly raised problematics of machine learning risks and ethics, with a focus on insurance and more generally on financial services, probably the most mature sectors, even if these problematics will soon affect all industries.

Play iconX-SHAP: towards multiplicative explainability of Machine Learning
by
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Yannick Léo
Yannick Léo
and
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Aimé Lachapelle
Aimé Lachapelle
and
,
Luisa Bouneder
Luisa Bouneder
May 2020

This paper introduces X-SHAP, a model-agnostic method that assesses multiplicative contributions of variables for both local and global predictions.

This methodtheoretically and operationally extends the so-called additive SHAP approach. Itproves useful underlying multiplicative interactions of factors, typically arisingin sectors where Generalized Linear Models are traditionally used, such as ininsurance or biology.

We test the method on various datasets and propose a set oftechniques based on individual X-SHAP contributions to build aggregated multiplicative contributions and to capture multiplicative feature importance, that wecompare to traditional techniques.

Play icon Healthcare Data Sharing: Fasten your seatbelt
by
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Aimé Lachapelle
Aimé Lachapelle
and
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Victor Pinault
Victor Pinault
March 2020

It is commonly acknowledged that healthcare data has tremendous potential as a tool to improve overall public health and unlock significant innovation in both the public and private healthcare sectors. Use cases based on the sharing and processing of healthcare data (e.g., machine learning, AI, etc.) are numerous.