Monte-Carlo methods are widely used by the financial industry to price derivatives, estimate risks, or to calibrate/estimate models. They can also be used to handle big data, in machine learning, to perform online optimization, to study the propagation of uncertainty in fluid mechanics or geophysics. Under the same label Monte-Carlo, one actually finds very different techniques and communities that evolve in different directions. The thematic cycle that we organized from october 2015 to July 2016 aimed at confronting the different viewpoints of these communities and at contributing to a general thinking on how these techniques can be used by the financial industry and the economic world in general. It benefited from the financial support of the Louis Bachelier Institute, the Chaire Risques Financiers, the Chaire Finance et D´eveloppement durable, the Chaire Economie des nou- ´ velles donn´ees, the Chaire March´es en mutation, the ANR program ISOTACE ANR-12-MONU-0013 and the Institut Henri Poincar´e. Three topics were covered by academic lectures followed by a one-day workshop: propagation of uncertainty, particle methods for the management of risks, stochastic algorithms and big data. We thank Areski Cousin, Virginie Ehrlacher, Romuald Elie, Gersende Fort, St´ephane Gaiffas and Gilles Pag`es for having coordinated these workshops. The cycle was concluded by a one week closing conference with twelve plenary talks and sixteen minisymposia: see the website https://montecarlo16.sciencesconf.org Of course the six papers in these proceedings cannot account for all the topics addressed during the cycle. But they give qualitative spotlights on some of the active fields of research on stochastic methods in finance. We thank their authors for these valuable contributions.