Social media offers an invaluable wealth of data to understand what is taking place in our society. However, the use of social media data to understand phenomena occurring in populations is difficult because the data we obtain is not representative and the tools which we use to analyze this data introduce hidden biases on characteristics such as gender or age. For instance, in France in 2021 women represent 51.6% of the population [1] whereas on Twitter they represent only 33.5% of the french users [2]. With such a difference between social networks user demographics and real population, detecting the gender or the age before going into a deeper analysis becomes a priority. In this paper we provide the results of an ongoing work on a comparative study between three different methods to estimate gender. Based on the results of the comparative study, we evaluate future work avenues.

After the crisis of 2008, and the important losses and shortfall in capital that it revealed, regulators conducted massive stress testing exercises in order to test the resilience of financial institutions in times of stress conditions. In this context, and considering the impact of these exercises on the banks' capital, organization and image, this white paper proposes a methodology that diffuses dynamically the stress on the credit rating scale while considering the performance of the credit score. Consequently, the aim is to more accurately reflect the impact of the stress on the portfolio by taking into account the purity of the score and its ability to precisely rank the individuals of the portfolio.