Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

Mapping the Evolution of "Clusters": A Meta-analysis

This paper presents a meta-analysis of the “cluster literature” contained in scientific journals from 1969 to 2007. Thanks to an original database we study the evolution of a stream of literature which focuses on a research object which is both a theoretical puzzle and an empirical widespread evidence. We identify different growth stages, from take-off to development and maturity. We test the existence of a life-cycle within the authorships and we discover the existence of a substitutability relation between different collaborative behaviours. We study the relationships between a “spatial” and an “industrial” approach within the textual corpus of cluster literature and we show the existence of a “predatory” interaction. We detect the relevance of clustering behaviours in the location of authors working on clusters and in measuring the influence of geographical distance in co-authorship. We measure the extent of a convergence process of the vocabulary of scientists working on clusters.Cluster, Life-Cycle, Cluster Literature, Textual Analysis, Agglomeration, Co-Authorship

Using boosting for automated planning and trading systems

The problem: Much of finance theory is based on the efficient market hypothesis. According to this hypothesis, the prices of financial assets, such as stocks, incorporate all information that may affect their future performance. However, the translation of publicly available information into predictions of future performance is far from trivial. Making such predictions is the livelihood of stock traders, market analysts, and the like. Clearly, the efficient market hypothesis is only an approximation which ignores the cost of producing accurate predictions. Markets are becoming more efficient and more accessible because of the use of ever faster methods for communicating and analyzing financial data. Algorithms developed in machine learning can be used to automate parts of this translation process. In other words, we can now use machine learning algorithms to analyze vast amounts of information and compile them to predict the performance of companies, stocks, or even market analysts. In financial terms, we would say that such algorithms discover inefficiencies in the current market. These discoveries can be used to make a profit and, in turn, reduce the market inefficiencies or support strategic planning processes. Relevance: Currently, the major stock exchanges such as NYSE and NASDAQ are transforming their markets into electronic financial markets. Players in these markets must process large amounts of information and make instantaneous investment decisions. Machine learning techniques help investors and corporations recognize new business opportunities or potential corporate problems in these markets. With time, these techniques help the financial market become better regulated and more stable. Also, corporations could save significant amount of resources if they can automate certain corporate finance functions such as planning and trading. Results: This dissertation offers a novel approach to using boosting as a predictive and interpretative tool for problems in finance. Even more, we demonstrate how boosting can support the automation of strategic planning and trading functions. Many of the recent bankruptcy scandals in publicly held US companies such as Enron and WorldCom are inextricably linked to the conflict of interest between shareholders (principals) and managers (agents). We evaluate this conflict in the case of Latin American and US companies. In the first part of this dissertation, we use Adaboost to analyze the impact of corporate governance variables on performance. In this respect, we present an algorithm that calculates alternating decision trees (ADTs), ranks variables according to their level of importance, and generates representative ADTs. We develop a board Balanced Scorecard (BSC) based on these representative ADTs which is part of the process to automate the planning functions. In the second part of this dissertation we present three main algorithms to improve forecasting and automated trading. First, we introduce a link mining algorithm using a mixture of economic and social network indicators to forecast earnings surprises, and cumulative abnormal return. Second, we propose a trading algorithm for short-term technical trading. The algorithm was tested in the context of the Penn-Lehman Automated Trading Project (PLAT) competition using the Microsoft stock. The algorithm was profitable during the competition. Third, we present a multi-stock automated trading system that includes a machine learning algorithm that makes the prediction, a weighting algorithm that combines the experts, and a risk management layer that selects only the strongest prediction and avoids trading when there is a history of negative performance. This algorithm was tested with 100 randomly selected S&P 500 stocks. We find that even an efficient learning algorithm, such as boosting, still requires powerful control mechanisms in order to reduce unnecessary and unprofitable trades that increase transaction costs

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

Effects of Logic-Style Explanations and Uncertainty on Users’ Decisions

The spread of innovative Artificial Intelligence (AI) algorithms assists many individuals in their daily life decision-making tasks but also sensitive domains such as disease diagnosis and credit risk. However, a great majority of these algorithms are of a black-box nature, bringing the need to make them more transparent and interpretable along with the establishment of guidelines to help users manage these systems. The eXplainable Artificial Intelligence (XAI) community investigated numerous factors influencing subjective and objective metrics in the user-AI team, such as the effects of presenting AI-related information and explanations to users. Nevertheless, some factors that influence the effectiveness of explanations are still under-explored in the literature, such as user uncertainty, AI uncertainty, AI correctness, and different explanation styles. The main goal of this thesis is to investigate the interactions between different aspects of decision-making, focusing in particular on the effects of AI and user uncertainty, AI correctness, and the explanation reasoning style (inductive, abductive, and deductive) on different data types and domains considering classification tasks. We set up three user evaluations on images, text, and time series data to analyse these factors on users' task performance, agreement with the AI suggestion, and the user’s reliance on the XAI interface elements (instance, AI prediction, and explanation). The results for the image and text data show that user uncertainty and AI correctness on predictions significantly affected users’ classification decisions considering the analysed metrics. In both domains (images and text), users relied mainly on the instance to decide. Users were usually overconfident about their choices, and this evidence was more pronounced for text. Furthermore, the inductive style explanations led to over-reliance on AI advice in both domains – it was the most persuasive, even when the AI was incorrect. The abductive and deductive styles have complex effects depending on the domain and the AI uncertainty levels. Instead, the time series data results show that specific explanation styles (abductive and deductive) improve the user’s task performance in the case of high AI confidence compared to inductive explanations. In other words, these styles of explanations were able to invoke correct decisions (for both positive and negative decisions) when the system was certain. In such a condition, the agreement between the user’s decision and the AI prediction confirms this finding, highlighting a significant agreement increase when the AI is correct. This suggests that both explanation styles are suitable for evoking appropriate trust in a confident AI. The last part of the thesis focuses on the work done with the \enquote{CRS4 - Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna}, for the implementation of the RIALE (Remote Intelligent Access to Lab Experiment) Platform. The work aims to help students explore a DNA-sequences experiment enriched with an AI tagging tool, which detects the objects used in the laboratory and its current phase. Further, the interface includes an interactive timeline which enables students to explore the AI predictions of the video experiment's steps and an XAI panel that provides explanations of the AI decisions - presented with abductive reasoning - on three levels (globally, by phase, and by frame). We evaluated the interface with students considering the subjective cognitive effort, ease of use, supporting information of the interface, general usability, and an interview on a set of questions on peculiar aspects of the application. The user evaluation results showed that students were positively satisfied with the interface and in favour of following didactic lessons using this tool

Sentiment Analysis for Fake News Detection

[Abstract] In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2020/11This work has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades — Agencia Estatal de Investigación through the ANSWERASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the Secretaría Xeral de Universidades (ref. ED431G 2019/01). David Vilares is also supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation. Carlos Gómez-Rodríguez has also received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant No. 714150

Generic Architecture for Predictive Computational Modelling with Application to Financial Data Analysis: Integration of Semantic Approach and Machine Learning

The PhD thesis introduces a Generic Architecture for Predictive Computational Modelling capable of automating analytical conclusions regarding quantitative data structured as a data frame. The model involves heterogeneous data mining based on a semantic approach, graph-based methods (ontology, knowledge graphs, graph databases) and advanced machine learning methods. The main focus of my research is data pre-processing aimed at a more efficient selection of input features to the computational model. Since the model I propose is generic, it can be applied for data mining of all quantitative datasets (containing two-dimensional, size-mutable, heterogeneous tabular data); however, it is best suitable for highly interconnected data. To adapt this generic model to a specific use case, an Ontology as the formal conceptual representation for the relevant domain knowledge is needed. I have determined to use financial/market data for my use cases. In the course of practical experiments, the effectiveness of the PCM model application for the UK companies’ financial risk analysis and the FTSE100 market index forecasting was evaluated. The tests confirmed that the PCM model has more accurate outcomes than stand-alone traditional machine learning methods. By critically evaluating this architecture, I proved its validity and suggested directions for future research