WHO ARE WE?
WHAT DO WE DO?
HEAD OF THE SQUARE RESEARCH CENTER
Our work demonstrates the limitations of Big Data in the field of customer knowledge. It has paved the way for new research avenues on Machine Learning, especially Preference Learning. Applied to target marketing, this analytical technique makes it possible to characterize profit-generating customers and can be extended to other fields (health, etc.).
A digital twin is a type of simulation that relies on the principle of a virtual clone of a physical system or process. While digital twins are broadly used in industrial and scientific settings to support product-associated decision-making, little research work has looked into the use of a digital twin to support process-associated decision-making. Management of the carbon footprint and greenhouse gas emissions in intralogistics is a process that would benefit from the digital twins we are currently developing.
We work on improving the integration of the non-stationary and multivariate nature of climate variables in the statistics field. Starting from the newly established mathematical framework, we allow non-life insurers to evaluate their risks pertaining to climate change by considering the inter-dependencies of risks.
We are developing a new, complete conceptual framework to make the analysis of investment portfolio more objective and thus create a series of models able to integrate and process data on an organization’s greenhouse gas emissions ; to adapt the projection methods of gases emissions to the data available, and to rely on mixed methods to optimize such projections ; to bring transparency to the operation and organization of the algorithms underlying the model.
A value-based approach enables decision-makers to develop a strategy integrated across their entire value network (ecosystem), encompassing performance stakes (financial, extra-financial, operational) as well as the most modern challenges of competitivity, innovation and profitability. Managing performance trough value enables executive boards to align strategy, organization, operations and management.
Our research work aims to provide a new reference framework for designing and managing new business model projects and determining to which extent platformization can — or cannot — be a good development lever. We especially focus on the platformization perspectives in the context of Open Banking, and their impact on value creation models.
We have developed an econometric modelling to analyze the determining factors (both exogenous and endogenous) impacting profitability and banking risks. Thereby interrogating the durability of the banking business models, formerly called “banking sustainability”, our work offers a new analytic framework to measure and anticipate impacts on the profitability and banking risk profile, depending on their specificities: retail bank, investment bank, mass retail bank, universal bank.
Our R&D program aims to develop a tool that measures the immaterial value of digital technologies, thus ultimately delivering an analysis and decision-aiding tool. We allow responsible digital technologies to be an integral part of the CSR and IT strategies of organizations, thus consolidating the management of extra-financial performance.
We strengthen the capacity of organizations to grasp and evaluate the cultural factors that are directly and indirectly impacting their ecosystem as well as their capacity to effectively change and manage such transformation. Organizational culture analysis, in particular, often remains relegated to the background, making any cultural diagnostic difficult, or even useless, thereby depriving organizations from an instrumental lever to promote the chosen change.
We are developing a model of an approach to understand the way in which business practices are applied in organizations. The program aims to model the best possible way to set people, teams and the eventually the whole organization in motion, and thereby lead the change depending on an organization’s practices and structure.
Our research work is focusing on developing solutions to improve the evaluation metrics of interpretability models in terms of plausibility and fidelity. The design of robust substitution models against adversarial attacks. The generation of adversarial attacks and suitable counterfactuals, which are especially difficult for NLP applications, to eventually optimize the interpretability of AI algorithms.
Our research work aims to develop innovative statistical and mathematical models contributing the modelling of the impact of climate and transition risks on the solvency of banks and on the calculation of their capital needs to cover the risks induced by climate change.
Our research work is developing an alternative model to the corporate social responsibility, which can be termed “Contributive Strategy for the Advent of Sustainable Development (Stratégie de Contribution à l’Avènement du Développement Durable, SCAD), and specific methodology for the support of SCADs towards sustainability.
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