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.
Model Risk in the Age of Artificial Intelligence and Machine LearningAn 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.
