144 research outputs found

    Corporate Bankruptcy Prediction

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    Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy

    Data-Driven Implementation To Filter Fraudulent Medicaid Applications

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    There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures

    Data-Driven Implementation To Filter Fraudulent Medicaid Applications

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    There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures

    A Corpus Driven Computational Intelligence Framework for Deception Detection in Financial Text

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    Financial fraud rampages onwards seemingly uncontained. The annual cost of fraud in the UK is estimated to be as high as £193bn a year [1] . From a data science perspective and hitherto less explored this thesis demonstrates how the use of linguistic features to drive data mining algorithms can aid in unravelling fraud. To this end, the spotlight is turned on Financial Statement Fraud (FSF), known to be the costliest type of fraud [2]. A new corpus of 6.3 million words is composed of102 annual reports/10-K (narrative sections) from firms formally indicted for FSF juxtaposed with 306 non-fraud firms of similar size and industrial grouping. Differently from other similar studies, this thesis uniquely takes a wide angled view and extracts a range of features of different categories from the corpus. These linguistic correlates of deception are uncovered using a variety of techniques and tools. Corpus linguistics methodology is applied to extract keywords and to examine linguistic structure. N-grams are extracted to draw out collocations. Readability measurement in financial text is advanced through the extraction of new indices that probe the text at a deeper level. Cognitive and perceptual processes are also picked out. Tone, intention and liquidity are gauged using customised word lists. Linguistic ratios are derived from grammatical constructs and word categories. An attempt is also made to determine ‘what’ was said as opposed to ‘how’. Further a new module is developed to condense synonyms into concepts. Lastly frequency counts from keywords unearthed from a previous content analysis study on financial narrative are also used. These features are then used to drive machine learning based classification and clustering algorithms to determine if they aid in discriminating a fraud from a non-fraud firm. The results derived from the battery of models built typically exceed classification accuracy of 70%. The above process is amalgamated into a framework. The process outlined, driven by empirical data demonstrates in a practical way how linguistic analysis could aid in fraud detection and also constitutes a unique contribution made to deception detection studies

    A bundle of services to develop better Machine Learning applications

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    Inteligência Artificial (IA) é um tema na moda atualmente. Machine Learning (ML) é a área mais comum de aplicação de IA, e como o nome indica, o objetivo é fazer com que a máquina aprenda. Essa aprendizagem pode ser a simulação de tarefas repetitivas do Homem, para, por exemplo, testar cenários hipotéticos ou até mesmo substituir a mão de obra humana. Pode inclusivamente, ser uma simulação a nível físico como a nível mental, ou seja, envolver o deslocamento de algum objeto, ou ainda o raciocínio ou o resultado deste de um indivíduo. Estes sistemas inteligentes podem até superar o intelecto do Homem. No entanto, é necessário haver restrições da sua aplicação em determinados domínios mais sensíveis onde exista um direito à explicação, como refere o Regulamento Geral sobre a Proteção de Dados 2016/679 (RGPD), em que qualquer decisão que tenha por base um sistema inteligente tem de ser justificada. Como refere o Regulamento Europeu para a Inteligência Artificial, principalmente no ponto 3.5, o uso de IA pode afetar significativamente um elevado número de fatores relacionado com os direitos fundamentais do ser humano. Existe, portanto, a necessidade de assegurar o direito à dignidade humana, respeito pela privacidade, não discriminação e igualdade de género. É necessário garantir também que todos os intervenientes afetados por um sistema de IA tenham as mesmas condições de trabalho e de segurança. De facto, grande parte das aplicações de ML têm como intuito auxiliar o ser humano, como, por exemplo, ajudar o gestor de alguma empresa a tomar uma decisão e/ou explicá-la. O problema é que os algoritmos conhecidos por oferecerem uma melhor performance, tais como redes neuronais que são uma abordagem inspirada no funcionamento do sistema nervoso dos mamíferos, são também aqueles cujo funcionamento ou o porquê de tomarem determinadas previsões é mais difícil de decifrar. Nesse sentido, motivado pelas novas normas do RGPD e por questões éticas, e com um caso real de aplicação no domínio de deteção de fraude fiscal, um dos objetivos deste trabalho é explicar o porquê das previsões elaboradas pelos algoritmos conhecidos por black-box. Não obstante, o trabalho pode ser aplicado a outros algoritmos em que falte a componente explicativa, e outros domínios que necessitem de uma decisão apoiada numa explicação. A solução proposta é o desenvolvimento de raiz de um sistema inteligente na área XAI (Explainable Artificial Intelligence), que seja incorporado e contribua para um sistema de ML já existente com justificações plausíveis e transparentes sobre as previsões dadas por outros modelos de ML. Outro desafio destes sistemas inteligentes é a necessidade de um constante retreino de modelos, dado que novos dados chegam ao sistema, para não ficarem obsoletos com o tempo por já não conseguirem eficazmente realizar uma previsão. Contudo, uma maior quantidade de dados não significa necessariamente novos padrões, correndo-se o risco de se desperdiçar recursos a re-treinar um modelo cuja performance não é superior à sua anterior versão. Para abordar este problema, propõe-se o uso de meta-learning para prever a performance de um modelo de ML com base nas características do dataset (caracterizadas por meta-features). Resumidamente, será construído um meta-modelo com base nas meta-features de vários datasets, que terá a capacidade de prever uma métrica de erro de um futuro modelo de ML, e.g. RMSE, MSE, R², MAE, incluindo o tempo que demora a treinar o modelo, permitindo assim decidir quanto ao re-treino ou não do modelo. Este conjunto de serviços para ML permitirá desenvolver melhores modelos, quer do ponto de vista ético, quer do ponto de vista da sua eficiência.Artificial Intelligence (AI) is a fashionable topic these days. Machine Learning is the most common application of AI, and it’s widely used in many domains in order to give predictions and help a decision agent to decide. The problem is that the algorithms that are known to offer better performance, such as neural networks, inspired by the human brain, are also more difficult to understand. Therefore, it’s necessary to have restrictions of its application in more sensitive areas in which there is a right to an explanation, as stated in the General Data Protection Regulation 2016/679 (GDPR). In this sense, motivated by the new rules of the GDPR and by ethical issues, one of the objectives of this work is to explain why of such algorithms known as black-box make certain predictions. The proposed approach can be applied to other algorithms that lack the explanatory component, and in domains that need a decision supported by an explanation. The proposed solution is the development of an intelligent system from scratch in the field of Explainable Artificial Intelligence (XAI), which is incorporated into and contributes to an existing ML system with plausible and transparent justifications about the predictions given by the other ML models. Another challenge of these intelligent systems is the need for constant re-training of models, given that new data are always arriving, so that they don’t become obsolete over time. However, a greater amount of data not necessarily means new patterns, running the risk of wasting resources on re-training a model whose performance is not better to its previous version. To address this problem, we propose the use of meta-learning to predict the performance of a ML model based on dataset characteristics (meta-features). In short, a Meta-model is constructed based on the meta-features of many datasets, which will have the ability to predict an error metric of a future ML model, e.g. RMSE, MSE, R², MAE, including the time it takes to train the model, thus making it possible to decide on re-training or not the model. This set of services for ML will allow developing better ML systems, from the point of ethical view and its efficienc

    A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

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    Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

    eXplainable AI for trustworthy healthcare applications

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    Acknowledging that AI will inevitably become a central element of clinical practice, this thesis investigates the role of eXplainable AI (XAI) techniques in developing trustworthy AI applications in healthcare. The first part of this thesis focuses on the societal, ethical, and legal aspects of the use of AI in healthcare. It first compares the different approaches to AI ethics worldwide and then focuses on the practical implications of the European ethical and legal guidelines for AI applications in healthcare. The second part of the thesis explores how XAI techniques can help meet three key requirements identified in the initial analysis: transparency, auditability, and human oversight. The technical transparency requirement is tackled by enabling explanatory techniques to deal with common healthcare data characteristics and tailor them to the medical field. In this regard, this thesis presents two novel XAI techniques that incrementally reach this goal by first focusing on multi-label predictive algorithms and then tackling sequential data and incorporating domainspecific knowledge in the explanation process. This thesis then analyzes the ability to leverage the developed XAI technique to audit a fictional commercial black-box clinical decision support system (DSS). Finally, the thesis studies AI explanation’s ability to effectively enable human oversight by studying the impact of explanations on the decision-making process of healthcare professionals

    Advanced analytical methods for fraud detection: a systematic literature review

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    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

    Can bank interaction during rating measurement of micro and very small enterprises ipso facto Determine the collapse of PD status?

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    This paper begins with an analysis of trends - over the period 2012-2018 - for total bank loans, non-performing loans, and the number of active, working enterprises. A review survey was done on national data from Italy with a comparison developed on a local subset from the Sardinia Region. Empirical evidence appears to support the hypothesis of the paper: can the rating class assigned by banks - using current IRB and A-IRB systems - to micro and very small enterprises, whose ability to replace financial resources using endogenous means is structurally impaired, ipso facto orient the results of performance in the same terms of PD assigned by the algorithm, thereby upending the principle of cause and effect? The thesis is developed through mathematical modeling that demonstrates the interaction of the measurement tool (the rating algorithm applied by banks) on the collapse of the loan status (default, performing, or some intermediate point) of the assessed micro-entity. Emphasis is given, in conclusion, to the phenomenon using evidence of the intrinsically mutualistic link of the two populations of banks and (micro) enterprises provided by a system of differential equation
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