Data mining for detecting Bitcoin Ponzi schemes

Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives

ICT and elections in Nigeria: rural dynamics of biometric voting technology adoption

Applications of Information and Communications Technology (ICT)-driven innovations are profound in the electoral cycle. Among them, biometric technology is currently sweeping across developing countries. It is, however, only poorly adopted among rural voters. Does the use of biometric technology in the conduct of elections reconstruct rural voters’ behaviour, amid prevailing social challenges? The links between these realities and their consequences are currently less understood, and lacking in supporting literature. I argue that the public perception of biometric technology, the availability of proper infrastructure, and the distance between polling stations and the dwellings of rural voters all affect the latter's level of adoption of biometric technology. These interactions combine to produce specific modalities that shape voting behaviour and general political culture. I elicit primary data from voters in Nigeria’s remote villages, so as to predict the implications and consequences of glossing over the dimensions and magnitude of the biometric technology adaptation challenge by policymakers. I conclude by reflecting on how these interplays and interactions create "spatial differentials" in electoral outcomes/credibility, and proffer possible strategies for institutional intervention.Die Anwendungen von Innovationen im Bereich der Informations- und Kommunikationstechnologie (IKT) sind im Wahlzyklus von großer Bedeutung. Biometrische Technologie erobert derzeit die Entwicklungsländer. Sie wird aber von den Wählern auf dem Land nur schlecht angenommen. Ändert die Nutzung von biometrischer Technologie das Wahlverhalten der Bevölkerung auf dem Land vor dem Hintergrund sozialer Herausforderungen? Der Zusammenhang zwischen diesen Realitäten und ihren Folgen wird in der Literatur noch nicht umfassend behandelt. Der Artikel argumentiert, dass die öffentliche Wahrnehmung der biometrischen Technologie, die Verfügbarkeit einer geeigneten Infrastruktur und die Entfernung zwischen den Wahllokalen und den Siedlungen der Wähler auf dem Land allesamt beeinflussen, inwieweit die ländliche Bevölkerung solche Technologien annimmt. Dieses Zusammenspiel führt zu spezifischen Modalitäten, die das Wahlverhalten und die allgemeine politische Kultur prägen. Ich nutze Primärdaten aus abgelegenen Dörfern in Nigeria, um zu zeigen, wie politische Entscheidungsträger Herausforderungen bei der Anwendung biometrischer Technologien schönreden und welche Folgen dies hat. Abschließend betrachte ich, wie diese Wechselwirkungen und Interaktionen zu "räumlichen Unterschieden" bei Wahlergebnissen/Glaubwürdigkeit führen und biete mögliche Strategien für institutionelle Interventionen an

Fighting Accounting Fraud through Forensic Analytics

Accounting Fraud is one of the most harmful financial crimes as it often results in massive corporate collapses, commonly silenced by powerful high-status executives and managers. Accounting fraud represents a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Its catastrophic consequences expose how vulnerable and unprotected the community is in regards to this matter, since most damage is inflicted to investors, employees, customers and government. Accounting fraud is defined as the calculated misrepresentation of the financial statement information disclosed by a company in order to mislead stakeholders regarding the firm’s true financial position. Different fraudulent tricks can be used to commit accounting fraud, either direct manipulation of financial items or creative methods of accounting, hence the need for non-static regulatory interventions that take into account different fraudulent patterns. Accordingly, this study aims to identify signs of accounting fraud occurrence to be used to, first, identify companies that are more likely to be manipulating financial statement reports, and second, assist the task of examination within the riskier firms by evaluating relevant financial red-flags, as to efficiently recognise irregular accounting malpractices. To achieve this, a thorough forensic data analytic approach is proposed that includes all pertinent steps of a data-driven methodology. First, data collection and preparation is required to present pertinent information related to fraud offences and financial statements. The compiled sample of known fraudulent companies is identified considering all Accounting Series Releases and Accounting and Auditing Enforcement Releases issued by the U.S. Securities and Exchange Commission between 1990 and 2012, procedure that resulted in 1,594 fraud-year observations. Then, an in-depth financial ratio analysis is performed in order to evaluate publicly available financial statement data and to preserve only meaningful predictors of accounting fraud. In particular, two commonly used statistical approaches, including non-parametric hypothesis testing and correlation analysis, are proposed to assess significant differences between corrupted and genuine reports as well as to identify associations between the considered ratios. The selection of a smaller subset of explanatory variables is later reinforced by the implementation of a complete subset logistic regression methodology. Finally, statistical modelling of fraudulent and non-fraudulent instances is performed by implementing several machine learning methods. Classical classifiers are considered first as benchmark frameworks, including logistic regression and discriminant analysis. More complex techniques are implemented next based on decision trees bagging and boosting, including bagged trees, AdaBoost and random forests. In general, it can be said that a clear enhancement in the understanding of the fraud phenomenon is achieved by the implementation of financial ratio analysis, mainly due to the interesting exposure of distinctive characteristics of falsified reporting and the selection of meaningful ratios as predictors of accounting fraud, later validated using a combination of logistic regression models. Interestingly, using only significant explanatory variables leads to similar results obtained when no selection is performed. Furthermore, better performance is accomplished in some cases, which strongly evidences the convenience of employing less but significant information when detecting accounting fraud offences. Moreover, out-of-sample results suggest there is a great potential in detecting falsified accounting records through statistical modelling and analysis of publicly available accounting information. It has been shown good performance of classic models used as benchmark and better performance of more advanced methods, which supports the usefulness of machine learning models as they appropriately meet the criteria of accuracy, interpretability and cost-efficiency required for a successful detection methodology. This study contributes in the improvement of accounting fraud detection in several ways, including the collection of a comprehensive sample of fraud and non-fraud firms concerning all financial industries, an extensive analysis of financial information and significant differences between genuine and fraudulent reporting, selection of relevant predictors of accounting fraud, contingent analytical modelling for better differentiate between non-fraud and fraud cases, and identification of industry-specific indicators of falsified records. The proposed methodology can be easily used by public auditors and regulatory agencies in order to assess the likelihood of accounting fraud and to be adopted in combination with the experience and instinct of experts to lead to better examination of accounting reports. In addition, the proposed methodological framework could be of assistance to many other interested parties, such as investors, creditors, financial and economic analysts, the stock exchange, law firms and to the banking system, amongst others

Insuring the African future

The African growth story has investors from around the world eyeing opportunities offered up by the continent in the form of new markets, enhanced growth potential and impressive returns. Despite the overwhelmingly positive thrust of this message, it finds itself situated against a backdrop of serious challenges, not only in Africa, but also globally, in the face of increasing financial, political and natural-catastrophe risk. In this world of tremendous risk and tremendous opportunity, the insurance industry can provide post-disaster financing, financial security, institutional investment and innovative risk management strategies to reduce levels of risk on the ground. Launched earlier this year, the Principles for Sustainable Insurance are a framework for embedding environmental, social and governance factors into insurance business and so promoting sustainable development. This creative research project argues that a robust insurance industry promotes economic growth and that the parallel developments, in the story of African growth and the risk management practices of the insurance industry, present a compelling framework for nurtured and sustainable development in Africa

Financial accounting calculation in relation to nature

PhD ThesisControversies drawn from the main thesis research question – asking what is the relationship between financial accounting calculation and nature – lead to the specification of research sub-questions. Firstly, how does financial accounting calculation communicate and/or construct the reality of humanity’s economic relationship with nature? Secondly, what is the role of financial accounting calculation in building markets for the purpose of addressing specific environmental problems? Thirdly, what kind of ontological relationship exists between financial accounting calculation and nature? These controversies are examined via two empirical case studies, utilising the principles of actor-network theory, and a conceptual discussion that draws from these cases and from literature on financial markets. The first empirical case study seeks to examine how the biodiversity comprising a tropical forest ecosystem in the Kasigau Corridor in Keyna is protected as a result of having its conservation brought into financial accounting calculations by constructing, via processes of objectification and singularisation, a greenhouse gas emissions offset product to sell on the voluntary over-the-counter carbon markets. The second empirical case study seeks to examine the performativity of financial accounting in the construction of markets in tropical forest carbon. The analysis describes and explains the conflicts surrounding the translation of carbon market calculative devices by networks of organisational actors to extract a tradable accounting inscription from the world of tropical forests. A conceptual discussion then places economic markets on a flat ontological landscape with natural systems. This theoretical conception allows for a direct comparison between the roles of financial accounting calculations in markets and that of other forms of calculation and emergent computation in natural systems, finding that they are ontologically equivalent. This then provides a new theoretical frame for considering issues such as pluralism of accountings and accounting for sustainability

The semantics of psychospace

Traditionally, in the landscape profession, landscape analysis has been concerned with the physical aspects of place. Properties like shape, amount, use, colour and content have been surveyed, identified and classed in their various combinations to describe ' place character '. With few exceptions, ( Appleton 1998 ), the psychological aspects of place as criteria for classification have been largely ignored. One of the reasons for this, has been the argument that such data are' subjective' and personal, when what is required is, ' objective', verifiable and subject to 'constancy'. Another equally valid objection has been the difficulty in defining and identifying the psychological properties of place.The proposed method of analysing places by their psychological properties depends on people being able to verbally describe their feelings and states of mind. To define the survey parameters, these personal , emotional and mental properties have been identified and arranged in spectrums. By selecting the appropriate terms to describe their feelings in place, psychological profiles can be prepared, describing person -place relationships. With many such profiles, linked to personal details, like age, activity, sex and culture, factor analysis allows statistical examinations to be made of these person -place relationships. These reveal consistent patterns, relating particular combinations of feelings to particular combinations of perceivable place properties.Language is the medium of analysis and a linguistic examination of the data allows its classification into different types of place property. Those which are tangible, nominals and nouns, like apples, beds and chairs, and those which are intangible and descriptors, like abnormality, banality and chaos. Linguistics also offers, through concepts like antonymy, the ability to express opposites or contrasts in design terms, like, alien -friendly, bold -weak, chaotic- ordered.Certain combinations of emotions and perceivable, intangible place properties indicate places of particular significance. These are defined as archetypes. Thus, Arcadia is emotionally peaceful, restful and tranquil, and perceivably fertile, productive and beautiful. Battlefield is tense, shocking, stressful and perceivably brutal, chaotic and dramatic.CG Jung, (1968) asserted that anthropomorphic archetypes exist in the 'collective unconscious' of society and that this innate knowledge prepares the mind for future encounters. His archetypes included concepts like Mother and Father, Superman and Hero. By extension, it is postulated that places are also archetypal.To relate people to places objectively, the concept of 'objective relativity' is evoked ( G H Mead. 1932), allowing personal properties like awe, beauty and calmness to be logically attributed to place, relative to particular people.The main concept on which the thesis is based, is 'Psychospace', a linguistic model of the total psychological experience of place. New concepts are created to describe further people - place relationships. Pratties are property feelings of people attributed to place and Percies are properties of place perceived by some people and not others, and therefore 'subjective', like order, chaos and formality.Also included in 'subjective' judgements are those of assessment. Procons are personal properties, like quality and value, good, bad and satisfactory, but also objectively relative.Methods are proposed for the analysis of places and people and the identification of concepts which are employed in the processes of design. Examples are shown and discussed of how the formulated principles work in practice

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested