Aimé Lachapelle is Managing Partner of Emerton Data. He has over 15 years of experience in initiating and leading large Data & Artificial Intelligence projects in multiple environments: from the start-up (MFG Labs) to the large corporation (AXA), and to digital consulting.
Before co-founding Emerton Data, Aimé was Principal & Director of Data Science at Capgemini Invent, where he advised large organizations regarding the design and the implementation of data transformation strategies. He conducted end-to-end projects involving data and technology, focusing on value delivering.
Aimé has a deep expertise digital & data strategy, data factory set-up, data-driven pricing, supply chain, quantitative marketing, advanced analytics and data science.
He has in-depth sector knowledge in insurance & bank, agri-food industry, energy, aero industry, and consumer goods.
Aimé holds a Ph.D. in Applied Mathematics from Université Paris-Dauphine, followed by a 1-year post-doctorate at Princeton University. He published several scientific articles in first class peer-reviewed journals. Aimé’s research has always been very close to real world applications. Since he is in the business, he is convinced that there is an under-used potential to build fruitful collaborations between the business and academia. He launched three such initiatives in data science, two of them are currently leading to innovative start-up businesses.
AI is projected to enhance HR productivity by 30 to 40%, catalyzing its strategic evolution within the company's framework.
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.
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!
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?
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.
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.
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.
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.
