The european green deal impact on the competitiveness of the agricultural sector in Portugal

This dissertation is a pedagogical case study focused on the impacts that the European Green Deal will have at EU level in its primary sector, namely, in Portugal. A variety of concepts such as sustainability, competitiveness and internationalization will be studied and applied to the following hypothesis: how can the Portuguese primary sector companies, in light of the increased cost structure, remain competitive? The objective of this thesis is to understand, first, at a microeconomic level, the different factors that will have an impact on the cost structure of the farmers, like the higher costs which flow from the investments needed in new technologies. Secondly, at a macroeconomic level, an overview over the current exports and imports and its forecast will be discussed. It was concluded that the farmers will face some challenges and in order to ensure the success of the European Green Deal, the latter must be addressed by different entities. There will be an increase in costs in the short term which will need to be funded with the respective funding options available, for instance, the CAP and the Horizon Europe. In the long term, it is of extreme importance that the consumer’s mindset adapts to more sustainable diets. Further, so that the internationalization of European products thrives, there needs to be a clearer regulation so that the farmers are aware of which products they can export or import to regions which are not in scope of these regulations.Esta tese de mestrado consiste num caso pedagógico focado nos impactos que o Pacto Ecológico Europeu terá a nível da EU no seu setor primário, nomeadamente em Portugal. Diversos conceitos como sustentabilidade, competitividade e internacionalização serão estudados e aplicados à seguinte hipótese: como é que as empresas portuguesas do setor primário, face ao aumento da estrutura de custos, podem manter-se competitivas? O objetivo desta tese é compreender, em primeiro lugar, ao nível microeconómico, os diferentes fatores que terão impacto na estrutura de custos dos agricultores, como os custos mais elevados decorrentes dos investimentos necessários em novas tecnologias. Em segundo lugar, a nível macroeconómico, será discutida uma visão geral sobre as exportações e importações atuais e a sua previsão. Foi concluído que os agricultores irão enfrentar alguns desafios e para garantir o sucesso do Pacto Ecológico Europeu, este último deve ser abordado por diferentes entidades. Haverá um aumento de custos a curto prazo que terá de ser financiado com as respetivas opções de financiamento disponíveis, como por exemplo, a PAC e o Horizonte Europa, e a longo prazo é de extrema importância que a mentalidade do consumidor se adapte a dietas mais sustentáveis. Além disso, para que a internacionalização dos produtos europeus prospere, é necessária uma regulamentação mais clara para que os agricultores saibam quais produtos podem exportar ou importar para regiões que não estão no alcance dessas regulamentações

LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering

Anti-money laundering (AML) regulations mandate financial institutions to deploy AML systems based on a set of rules that, when triggered, form the basis of a suspicious alert to be assessed by human analysts. Reviewing these cases is a cumbersome and complex task that requires analysts to navigate a large network of financial interactions to validate suspicious movements. Furthermore, these systems have very high false positive rates (estimated to be over 95\%). The scarcity of labels hinders the use of alternative systems based on supervised learning, reducing their applicability in real-world applications. In this work we present LaundroGraph, a novel self-supervised graph representation learning approach to encode banking customers and financial transactions into meaningful representations. These representations are used to provide insights to assist the AML reviewing process, such as identifying anomalous movements for a given customer. LaundroGraph represents the underlying network of financial interactions as a customer-transaction bipartite graph and trains a graph neural network on a fully self-supervised link prediction task. We empirically demonstrate that our approach outperforms other strong baselines on self-supervised link prediction using a real-world dataset, improving the best non-graph baseline by $12$ p.p. of AUC. The goal is to increase the efficiency of the reviewing process by supplying these AI-powered insights to the analysts upon review. To the best of our knowledge, this is the first fully self-supervised system within the context of AML detection.Comment: Accepted at ACM International Conference on AI in Finance 2022 (ICAIF'22

FairGBM: Gradient Boosting with Fairness Constraints

Machine Learning (ML) algorithms based on gradient boosted decision trees (GBDT) are still favored on many tabular data tasks across various mission critical applications, from healthcare to finance. However, GBDT algorithms are not free of the risk of bias and discriminatory decision-making. Despite GBDT's popularity and the rapid pace of research in fair ML, existing in-processing fair ML methods are either inapplicable to GBDT, incur in significant train time overhead, or are inadequate for problems with high class imbalance. We present FairGBM, a learning framework for training GBDT under fairness constraints with little to no impact on predictive performance when compared to unconstrained LightGBM. Since common fairness metrics are non-differentiable, we employ a "proxy-Lagrangian" formulation using smooth convex error rate proxies to enable gradient-based optimization. Additionally, our open-source implementation shows an order of magnitude speedup in training time when compared with related work, a pivotal aspect to foster the widespread adoption of FairGBM by real-world practitioners

Promoting Fairness through Hyperparameter Optimization

Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce. Existing methods are either metric- or model-specific, require access to sensitive attributes at inference time, or carry high development or deployment costs. This work explores the unfairness that emerges when optimizing ML models solely for predictive performance, and how to mitigate it with a simple and easily deployed intervention: fairness-aware hyperparameter optimization (HO). We propose and evaluate fairness-aware variants of three popular HO algorithms: Fair Random Search, Fair TPE, and Fairband. We validate our approach on a real-world bank account opening fraud case-study, as well as on three datasets from the fairness literature. Results show that, without extra training cost, it is feasible to find models with 111% mean fairness increase and just 6% decrease in performance when compared with fairness-blind HO.Comment: arXiv admin note: substantial text overlap with arXiv:2010.0366