Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System

Guerra, P., Castelli, M., & Côrte-Real, N. (2022). Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System. Risks, 10(4), 1-23. [71]. https://doi.org/10.3390/risks10040071Risk analysis and scenario testing are two of the core activities carried out by economists at central banks. With the increasing adoption of machine learning to enhance decision-support systems, and the amount of collected data spiking, institutions provide countless use-cases for the application of these innovative technologies. Consequently, in recent years, the term sup-tech has entered the financial jargon and is here to stay. In this paper, we address risk assessment from a central bank’s perspective. The uptrending number of involved banks and institutions raises the necessity of a standardised risk methodology. For that reason, we adopted the Risk Assessment Methodology (RAS), the quantitative pillar from the Supervisory Review and Evaluation Process (SREP). Based on real-world supervisory data from the Portuguese banking sector, from March 2014 until August 2021, we successfully model the supervisory risk assessment process, in its quantitative approach by the RAS. Our findings and the resulting model are proposed as an Early Warning System that can support supervisors in their day-to-day tasks, as well as within the SREP process.publishersversionpublishe

is data quality a way to extract business value?

Côrte-Real, N., Ruivo, P., & Oliveira, T. (2020). Leveraging internet of things and big data analytics initiatives in European and American firms: is data quality a way to extract business value? Information and Management, 57(1), 1-16. [103141]. [Advanced online publication on 7 January 2019] doi: https://doi.org/10.1016/j.im.2019.01.003Big data analytics (BDA) and the Internet of Things (IoT) tools are considered crucial investments for firms to distinguish themselves among competitors. Drawing on a strategic management perspective, this study proposes that BDA and IoT capabilities can create significant value in business processes if supported by a good level of data quality, which will lead to a better competitive advantage. Responses are collected from 618 European and American firms that use IoT and BDA applications. Partial least squares results reveal that better data quality is needed to unlock the value of IoT and BDA capabilities.authorsversionpublishe

Unlocking the drivers of big data analytics value in firms

Côrte-Real, N., Ruivo, P., Oliveira, T., & Popovič, A. (2019). Unlocking the drivers of big data analytics value in firms. Journal of Business Research, 97(April), 160-173. DOI: 10.1016/j.jbusres.2018.12.072Although big data analytics (BDA) is considered the next “frontier” in data science by creating potential business opportunities, the way to extract those opportunities is unclear. This paper aims to understand the antecedents of BDA value at a firm level. The authors performed a study using a mixed methodology approach. First, by carrying out a Delphi study to explore and rank the antecedents affecting the creation of BDA value. Based on the Delphi results, we propose an empirically validated model supported by a survey conducted on 175 European firms to explain the antecedents of BDA sustained value. The results show that the proposed model explains 62% of BDA sustained value at the firm level, where the most critical contributor is BDA use. We provide directions for managers to support their decisions on BDA strategy definition and refinement. For academics, we extend BDA value literature and outline some potential research opportunities.authorsversionpublishe

Vacuole-mitochondrial crosstalk during apoptosis induced by acetic acid in yeast

This work was supported by the FCT project PTDC/BIA-BCM/69448/200

What yeast can tell us about how cells commit suicide?

Multicellular organisms developed a complex system to balance cell proliferation and cell death in order to guarantee correct embryonic development and tissue homeostasis. Failure of cells to undergo programmed cell death (PCD) can potentially lead to severe diseases, including neural degeneration, autoimmunity and cancer. Identifying the molecules involved in PCD and understanding the regulation of the process are crucial for prevention and management of these diseases. Evidence of the enormous impact of PCD, of which apoptosis is the most frequent morphological phenotype, on human health makes it one of the today’s main research topics. Since PCD was initially considered specific of metazoans, biological models were first restricted to animal cells. Actually, based on the absence of known crucial PCD regulators, as indicated by plain homologies searches, as well as on the difficulty to explain the sense of cell suicide in a unicellular organism, it was not accepted that these organisms could possess a PDC mechanism. However, evidence has been reported in the last decade indicating that the process of self-destruction in different unicellular organisms, namely in yeast, can also take place. In the present communication, I will present the research we have been developing on PCD, based on the exploration/exploitation of yeast as a simple eukaryotic unicellular model system. Particular focus will be given to our more recent studies suggesting a complex regulation and interplay between mitochondria and the vacuole in acetic acid induced PCD. The validation in mammalian cell lines of the hypothesis postulated with the yeast model will be also discussed.Fundação para a Ciência e a Tecnologia (FCT

Avaliação da maturidade da business intelligence nas organizações

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de InformaçãoA Business Intelligence (BI) tem um papel decisivo na criação de vantagens competitivas em qualquer organização (Evelson, Karel t al., 2010). Esta dissertação tem como principal intuito propor um modelo actual de maturidade de BI e a respectiva metodologia de avaliação. Para tal, foi iniciada a revisão de literatura que apresenta uma visão global da BI e a forma como esta influencia as organizações. Depois de uma breve exposição de conceitos e de uma visão global do funcionamento da BI, serão apresentados os factores que afectam a maturidade de BI que se constituem como as variáveis do modelo. serão igualmente expostos os modelos de maturidade que serviram de base para a criação do novo modelo. A metodologia de investigação proposta assenta num caso de estudo, propondo e apresentando os elementos necessários para desenvolver uma avaliação de maturidade de BI numa organização

Unlocking the real business value of big data analytics: from insight to firm performance

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision SystemsToday, we are living in a society where information technology (IT) is seen as integrant part of firm’s strategy in any business environment. Considered the next frontier of innovation, big data analytics (BDA) applications can potentially contribute to firm performance and create competitive advantage. Although there is an extant IT Value research, specific BDA Value research is underdeveloped. In addition, with the ever-increasing investment in BDA, it is essential to provide a valid and reliable measure to capture the business value that arises from the usage of this type of technologies. Grounded in strategic management theories (Knowledge-based View and Dynamic Capabilities), this dissertation aims to better understand the determinants of BDA value and impact on firm performance. By providing an integrative research model with three perspectives (Knowledge, Capabilities and Data), we intend to extend BDA value research and offer support to executives in their IT strategies. Following a positivist approach and hypothetic-deductive research methodology, the models were conceptualized and were empirically validated by qualitative and quantitative instruments in European and American firms, using Partial Least Squares (PLS) techniques for analysis. Findings demonstrate although empirical research on BDA value has been increasing in the latest years, empirical literature that examines how business value can be extracted is limited and has much room for improvement. Considering a capability point of view, factors related with BDA value sustainability such as BDA use, dynamic capabilities, agility, strategic alignment and environmental volatility are the most relevant to achieve competitive advantage when compared with managerial and operational variables. In this regard, a top-down approach is encouraged. From the knowledge perspective, external knowledge management is particularly relevant in the creation of BDA value for European firms. Also, BDA applications support a better internal and external knowledge management facilitating the creation of organizational agility in several levels. Agility has a positive mediation effect between internal and external knowledge management and firm performance. On the other side, sharing knowledge with partners in some cases can harm the creation of business value, specifically in core business areas such as Production and Operations or Product and Service enhancement. In the latest years, big data has been increasing exponentially with the emergence of IoT. Hence, it is relevant to assess the impact of both types of big data in firms. In this sense, from a data spectrum, data quality moderated by the level of sophistication on business processes has positive influence in the creation of BDA capabilities. On the contrary, it impacts negatively in the achievement of IoT capabilities. Due to the specificities of IoT big data and early stage of adoption, it is a challenge to ensure a reasonable level of data quality, which compromises the creation of IoT capabilities and consequent competitive advantage. There are no significant differences between U.S and EU firms in the creation of business value through BDA and IoT technologies.Atualmente vivemos numa sociedade onde as tecnologias de informação (TI) são consideradas parte integrante da estratégia das empresas qualquer que seja o contexto de negócio. As tecnologias de big data analytics (BDA) representam a próxima fronteira da inovação e podem potencialmente contribuir para a melhoria da performance empresarial e criação de vantagens competitivas. Apesar da investigação realizada sobre o valor das TI ser extensa, a área do valor de BDA encontra-se subdesenvolvido. Adicionalmente, com os investimentos crescentes em BDA, é essencial fornecer um instrumento válido e fiável que permita a mediação do valor de negócio que deriva da utilização deste tipo de tecnologias. Esta dissertação tem como objetivo, com base em teorias de gestão estratégica (Knowledge-based View and Dynamic Capabilities), percecionar os fatores determinantes do valor de BDA e o seu impacto na performance empresarial. Neste sentido, pretende-se estender esta linha de investigação e oferecer suporte aos executivos na definição de estratégias de TI, através da apresentação um modelo de investigação integrado com três perspetivas (Conhecimento, Competências e Dados). Os modelos foram conceptualizados e testados com instrumentos quantitativos e qualitativos em empresas europeias e americanas, com recurso a técnicas de análise de Partial Least Squares (PLS), seguindo uma abordagem positivista e uma metodologia hipotética-dedutiva. Conclui-se que apesar da investigação empírica sobre o valor de BDA estar a aumentar, ainda é bastante limitada. Do ponto de vista das competências, os fatores relacionados com a sustentabilidade do valor de BDA como a sua utilização, competências dinâmicas, agilidade, alinhamento estratégico e volatilidade do mercado são os mais relevantes para adquirir vantagens competitivas quando comparando com condições de gestão ou operacionais. Neste sentido, deve ser seguida uma abordagem top-down. Numa perspetiva de conhecimento, a gestão de conhecimento externo é particularmente relevante na criação de valor de BDA para as empresas europeias. As ferramentas de BDA suportam uma melhor gestão de conhecimento interno e externo o que facilita a criação de agilidade empresarial a vários níveis. A agilidade possui um efeito mediador positivo entre a gestão de conhecimento externo e interno e a performance empresarial. Por outro lado, partilhar conhecimento com parceiros de negócio pode em alguns casos ser prejudicial à criação de valor de negócio, nomeadamente em áreas de negócio primárias como Produção e Operações ou melhoria de Produtos e Serviços. Nos últimos anos, com o aparecimento do IoT, têm-se verificado um crescimento exponencial em big data. Assim, torna-se relevante avaliar o impacto dos dois tipos de big data nas empresas. Neste sentido, numa perspetiva de dados, a qualidade dos dados, moderada pelo nível de sofisticação dos processos possui uma influência positiva na criação de competências de BDA. Contrariamente, a qualidade dos dados tem um efeito negativo na aquisição de competências de IoT. Devido às características de IoT e o estágio inicial de adoção é um desafio assegurar um nível razoável de qualidade dos dados, o que compromete a criação de competências de IoT e a consequente obtenção de vantagens competitivas. Não existem diferenças significativas na forma como as empresas europeias e americanas utilizam BDA e IoT para criar valor de negócio

Machine learning for liquidity risk modelling: A supervisory perspective

Guerra, P., Castelli, M., & Côrte-real, N. (2022). Machine learning for liquidity risk modelling: A supervisory perspective. Economic Analysis and Policy, 74(June), 175-187. https://doi.org/10.1016/j.eap.2022.02.001The purpose of an effective liquidity risk assessment policy is to ensure that any given credit institution can meet its cash flow obligations, even factoring in the uncertainty caused by external factors. As part of the Supervisory Review and Evaluation Process (SREP), the European Central Bank (ECB) has determined this assessment should take into consideration both the institution’s ability to meet its short-term obligations and its long-term funding strategy. Due to the fast pace of financial markets and more demanding regulations, there is a structural need for a precise and widely accepted risk assessment methodology. Furthermore, the ability to foresee alternative scenarios by stressing the involved key risk indicators is of the utmost importance. This work investigates whether machine learning techniques can successfully model liquidity risk, thus providing insights for stress-testing scenarios. We have applied the Risk Assessment System (RAS) methodology to classify credit institutions from the Portuguese banking sector according to their liquidity risk, using real supervisory data (from 2014 until March 2021). We then studied the ability to model this risk classification, by comparing a series of well-established machine learning algorithms to a traditional statistical model for benchmarking. The results show that extreme gradient boosting (XGBoost) outperforms other methods for this classification problem. The resulting model can be set up for a production environment and provide scenarios for stress-testing, or as an early warning system (EWS), thus supporting the overall SREP exercise.authorsversionpublishe