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Supercharge your data wrangling with a graphics card
Graphics processing units aren’t just of interest to gamers and cryptocurrency miners. Increasingly, they’re being used to turbocharge academic research, too.
Graphics processing units aren’t just of interest to gamers and cryptocurrency miners. Increasingly, they’re being used to turbocharge academic research, too.
Journal Article
Data driven : an introduction to management consulting in the 21st century
\"This book is a \"scientific\" introduction to management consulting that covers elementary and more advanced concepts, such as strategy and client-relationship. It discusses the emerging role of information technologies in consulting activities and introduces the essential tools in data science, assuming no technical background. Drawing on extensive literature reviews with more than 200 peer reviewed articles, reports, books and surveys referenced, this book has at least four objectives: to be scientific, modern, complete and concise. An interactive version of some sections (industry snapshots, method toolbox) is freely accessible at econsultingdata.com.\"--Back cover.
Book
Spatial Solution to Measure Regional Efficiency — Introducing Spatial Data Envelopment Analysis
When investigating healthcare efficiency at the regional level, the problem of interactions between neighbouring locations arises. The health of the population in a given region is related to the healthcare in other areas through a medical tourism, a limited number of highly specialised institutions, competition between institutions, etc. Ignoring these inter-regional links may result in a systematic bias in the efficiency analysis. Similar issues may hinder any regional studies. Hence, the main purpose of this paper is to introduce a new approach to measuring efficiency in regional studies through spatial data envelopment analysis (SDEA). The paper offers a proper mathematical formulation of the new methodology and highlights differences between classic data envelopment analysis (DEA) and the newly developed method. The motivation for seeking a new solution to the problem of spatially adequate assessment of regional efficiency is derived from the literature review and a discussion of the presented theoretical examples. The classic DEA allows for multidimensional analysis of the performance of homogenous independent decision-making units. However, in regional studies, an area where DEA has gained popularity, the assumption of the isolation of decision-making units seems to be unfounded. In the SDEA approach, the region-specific spatial context is incorporated into the analysis via the W matrix and spatial interactions are reflected in the model through spatially weighted inputs and outputs. Therefore, in our paper, we verify the hypothesis that spatial interactions are an indispensable factor of regional efficiency analysis. A study of healthcare efficiency in European regions is presented as an illustration of the utility of the new methodology. Furthermore, we compare the results of the classic DEA approach with those of the SDEA, which is augmented with the spatial equivalents of inputs and outputs. Our results suggest that classic DEA undervalues regional healthcare efficiency by underestimating the region-specific spatial context. Researchers may find the introduced SDEA method useful in all space related fields when investigated phenomenon exhibits spatial autocorrelation. In particular, the new approach may deepen the regional efficiency analysis of innovation, development, logistics, tourism, etc.
Journal Article
Corruption red flags in public procurement: new evidence from Italian calls for tenders
This paper contributes to the analysis of quantitative indicators (i.e.,
red flags
or
screens
) to detect corruption in public procurement. It presents an approach to evaluate corruption risk in public tenders through standardized ML tools applied to detailed data on the content of calls for tenders. The method is applied to roadwork contracts in Italy and three main contributions are reported. First, the study expands the set of commonly discussed indicators in the literature to new ones derived from operative practices of police forces and the judiciary. Second, using novel and unique data on firm-level corruption risk, this study validates the effectiveness of the indicators. Third, it quantifies the increased corruption-prediction ability when indicators that are known to be unavailable to the corruption-monitoring authority are included in the prediction exercise. Regarding the specific red flags, we find a systematic association between high corruption risk and the use of multi-parameter awarding criteria. Furthermore, predictability of the red flag makes them ineffective as prediction tools: the most obvious and scrutinized red flags are either uncorrelated with corruption or, even, negatively associated with it, as it is the case for invoking special procedures due to “urgency,” or the extent of publicity of the call for tender.
Journal Article
The effect of anti-money laundering policies: an empirical network analysis
Aim
There is a growing literature analyzing money laundering and the policies to fight it, but the overall effectiveness of anti-money laundering policies is still unclear. This paper investigates whether anti-money laundering policies affect the behavior of money launderers and their networks.
Method
With an algorithm to match clusters over time, we build a unique dataset of multi-mode, undirected, binary, dynamic networks of natural and legal persons. The data includes ownership and employment relations and associated financial ties and is enriched with criminal records and police-related activities. The networks of money launderers, other criminals, and non-criminal individuals are analyzed and compared with temporal social network analysis techniques and panel data regressions on centrality measures, transitivity and assortativity indicators, and levels of constraint.
Findings
We find that after the announcement of the fourth EU anti-money laundering directive in 2015, money laundering networks show a significant increase in the use of foreigners and corporate structures. At the individual level, money launderers become more dominant in criminal clusters (increased closeness centrality). This paper shows that (the announcement of) anti-money laundering policies can affect criminal networks and how such effects can be tested.
Journal Article
The geometry of suspicious money laundering activities in financial networks
Corruption and organized crime are social problems that affect different communities around the world, involving public and private organizations in diverse sectors and activities. However, these problems are global phenomena that transcend economic, cultural, and social borders, especially, when corrupt individuals use the global financial system to protect their illegal money. This paper aims to evaluate the money laundering mechanism in financial networks, studying the structure of some suspicious money laundering groups, and how they could be detected by the use of topological and geometrical considerations that avoid the need of possibly non-available (or restricted) information.
Journal Article
Effective strategies for targeted attacks to the network of Cosa Nostra affiliates
Network dismantling has recently gained interest in the fields of intelligence agencies, anti-corruption analysts and criminal investigators due to its efficiency in disrupting the activity of malicious agents. Here, we apply this approach to detect effective strategies for targeted attacks to Cosa Nostra by analysing the collaboration network of affiliates that participate to the same crimes. We preliminarily detect statistically significant homophily patterns induced by being member of the same mafia syndicate. We also find that links between members belonging to different mafia syndicates play a crucial role in connecting the network into a unique component, confirming the relevance of weak ties. Inspired by this result we investigate the resilience properties of the network under random and targeted attacks with a percolation based toy model. Random removal of nodes results to be quite inefficient in dismantling the network. Conversely, targeted attacks where nodes are removed according to ranked network centralities are significantly more effective. A strategy based on a removal of nodes that takes into account how much a member collaborates with different mafia syndicates has an efficiency similar to the one where nodes are removed according to their degree. The advantage of such a strategy is that it does not require a complete knowledge of the underlying network to be operationally effective.
Journal Article