Explaining and Predicting Abnormal Expenses at Large Scale using Knowledge Graph based Reasoning

International audienceGlobal business travel spend topped record-breaking $1.2 Trillion USD in 2015, and will reach$1.6 Trillion by 2020 according to the Global Business Travel Association, the world's premier business travel and meetings trade organization. Existing expenses systems are designed for reporting expenses, their type and amount over pre-defined views such as time period, service or employee group. However such systems do not aim at systematically detecting abnormal expenses, and more importantly explaining their causes. Therefore deriving any actionable insight for optimising spending and saving from their analysis is time-consuming, cumbersome and often impossible. Towards this challenge we present AIFS, a system designed for expenses business owner and auditors. Our system is manipulating and combining semantic web and machine learning technologies for (i) identifying, (ii) explaining and (iii) predicting abnormal expenses claim by employees of large organisations. Our prototype of semantics-aware employee expenses analytics and reasoning, experimented with 191, 346 unique Accenture employees in 2015, has demonstrated scalability and accuracy for the tasks of explaining and predicting abnormal expenses

Oil Price Volatility, an Economic Determinant of Earnings Volatility - Empirical Analysis on Earnings Volatility of U.S. Oil and Gas Companies Between 1986-2016

This study examines whether oil price volatility, an economic determinant, has significant correla-tion with earnings volatility in the U.S. oil and gas companies. The study also explores whether earnings volatility has increased in the very industry for the last thirty years. Differences among sub groups within the industry are studied to add precision to the analysis. The study applies pooled data OLS regression to explore the relation between oil price volatility and earnings volatility. The observation sample is collected from Compustat database in WRDS from 1986 to 2016. Findings suggest that oil price volatility has positive relation with earnings volatility and cash flow of operations. Earnings volatility for the time frame from 2002 to 2016 is greater than before 2002 for the whole industry. The level of earnings volatility is larger for oil and gas producers(SIC1311) than for refineries(SIC2911) for both time periods. However, increasing degree of association be-tween the two variables is observed only for oil and gas producers(SIC1311). The study concludes that oil price volatility provides incremental information connected to earn-ings volatility associated risk in the U.S. oil and gas industry. Especially oil and gas producers were found significantly affected by oil price volatility in terms of earnings volatility

Overcoming over–indebtedness with AI - A case study on the application of AutoML to research

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThis research examines how artificial intelligence may contribute to better understanding and overcoming over-indebtedness in contexts of high poverty risk. This study uses a field database of 1,654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning generated three overindebtedness clusters: low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.33%). These served as basis for a better understanding on the complex issue that is over-indebtedness. Second, a predictive model was developed to serve as a tool for policymakers and advisory services by streamlining the classification of overindebtedness profiles. On building such model, an AutoML approach was leveraged achieving performant results (92.1% accuracy score). Furthermore, within the AutoML framework, two techniques were employed, leading to a deeper discussion on the benefits and inner workings of such strategy. Ultimately, this research looks to contribute on three fronts: theoretical, by unfolding previously unexplored characteristics on the concept of over-indebtedness; methodological, by proposing AutoML as a powerful research tool accessible to investigators on many backgrounds; and social, by building real-world applications that aim at mitigating over-indebtedness and, consequently, poverty risk

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy

Information Content and Interrelationships of Multiple Performance Measures

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An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

Antecedents of ESG-Related Corporate Misconduct: Theoretical Considerations and Machine Learning Applications

The core objective of this cumulative dissertation is to generate new insights in the occurrence and prediction of unethical firm behavior disclosure. The first two papers investigate predictors and antecedents of (severe) unethical firm behavior disclosure. The third paper addresses frequently occurring methodological issues when applying machine learning approaches within marketing research. Hence, the three papers of this dissertation contribute to two recent topics within the field of marketing: First, marketing research has already focused intensively on the consequences of corporate misconduct and the accompanying media coverage. Meanwhile, the prediction and the process of occurrence of such threatening events have been examined only sporadically so far. Second, companies and researchers are increasingly implementing machine learning as a methodology to solve marketing-specific tasks. In this context, the users of machine learning methods often face methodological challenges, for which this dissertation reviews possible solutions. Specifically, in study 1, machine learning algorithms are used to predict the future occurrence of severe threatening news coverage of corporate misconduct. Study 2 identifies relationships between the specific competitive situation of a company within its industry and unethical firm behavior disclosure. Study 3 addresses machine learning-based issues for marketing researchers and presents possible solutions by reviewing the computer science literature