Data Mining and Economic Crime Risk Management

2011 ◽  
pp. 205-227
Author(s):  
Mieke Jans ◽  
Nadine Lybaert ◽  
Koen Vanhoof
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Literature Review ◽  
Business Practice ◽  
Economic Crime ◽  
Academic World ◽  
Corporate Fraud ◽  
Current State ◽  
Crime Risk ◽  
A Current

Economic crime is a billion dollar business and is substantially present in our current society. Both researchers and practitioners have gone into this problem by looking for ways of fraud mitigation. Data mining is often called in this context. In this chapter, the application of data mining in the field of economic crime, or corporate fraud, is discussed. The classification external versus internal fraud is explained and the major types of fraud within these classifications will be given. Aside from explaining these classifications, some numbers and statistics are provided. After this thorough introduction into fraud, an academic literature review concerning data mining in combination with fraud is given, along with the current solutions for corporate fraud in business practice. At the end, a current state of data mining applications within the field of economic crime, both in the academic world and in business practice, is given.

Data Mining and Economic Crime Risk Management

Data Mining ◽  
2013 ◽  
pp. 1664-1686
Author(s):  
Mieke Jans ◽  
Nadine Lybaert ◽  
Koen Vanhoof
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Literature Review ◽  
Business Practice ◽  
Economic Crime ◽  
Academic World ◽  
Corporate Fraud ◽  
Current State ◽  
Crime Risk ◽  
A Current

Economic crime is a billion dollar business and is substantially present in our current society. Both researchers and practitioners have gone into this problem by looking for ways of fraud mitigation. Data mining is often called in this context. In this chapter, the application of data mining in the field of economic crime, or corporate fraud, is discussed. The classification external versus internal fraud is explained and the major types of fraud within these classifications will be given. Aside from explaining these classifications, some numbers and statistics are provided. After this thorough introduction into fraud, an academic literature review concerning data mining in combination with fraud is given, along with the current solutions for corporate fraud in business practice. At the end, a current state of data mining applications within the field of economic crime, both in the academic world and in business practice, is given.

scholarly journals A Review of Tools and Techniques for Data-Enabled Formative Assessment

2017 ◽  
Vol 46 (4) ◽  
pp. 505-526 ◽  
Author(s):  
Rob Nyland
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Literature Review ◽  
Formative Assessment ◽  
Real Time ◽  
Stream Data ◽  
Descriptive Data ◽  
Current State ◽  
State Of Research ◽  
Tools And Techniques

The purpose of this literature review is to understand the current state of research on tools that collect data for the purpose of formative assessment. We were interested in identifying the types of data collected by these tools, how these data were processed, and how the processed data were presented to the instructor or student for the purpose of formative assessment. We identified two categories of data: machine-graded and activity stream data. The data were processed using three methods: unprocessed activity streams, descriptive data analysis, and data mining. Processed data were presented to students through reports and real-time feedback and to instructors through reports and visual dashboards.

Self-leadership Training Review

2019 ◽  
Author(s):  
Andrew Sidwell ◽  
Michael Perry
Keyword(s):  
Leadership Training ◽  
Future Research ◽  
Training Methods ◽  
Current State ◽  
A Current

The purpose of this article was to examine the current state of self-leadership training. The authors analyzed all published, publicly available studies (in English) pertaining to self-leadership training methods, offering a current state of self-leadership training, and implications for future research.

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

scholarly journals Multi-Risk Assessment and Management—A Comparative Study of the Current State of Affairs in Chile and Ecuador

2021 ◽  
Vol 13 (3) ◽  
pp. 1366
Author(s):  
Stefan Greiving ◽  
Leonie Schödl ◽  
Karl-Heinz Gaudry ◽  
Iris Katherine Quintana Miralles ◽  
Benjamín Prado Larraín ◽  
...  
Keyword(s):  
Risk Management ◽  
Disaster Preparedness ◽  
Disaster Risk ◽  
International Strategy ◽  
Metropolitan Regions ◽  
Current State ◽  
Policy Goals ◽  
Development Goals ◽  
State Of Affairs ◽  
Output Indicators

In Chile and Ecuador, multiple hazards and dynamic processes in vulnerability pose a high risk. Spatial planning and emergency management can contribute to disaster risk management but they follow different goals. However, global goals, such as from UN-ISDR (United Nations International Strategy for Disaster Risk Reduction) and UN SDGs (Sustainable Development Goals) can potentially support cities and regions in defining concerted action. This paper aims at measuring the performance of Chile and Ecuador in regard to the aforementioned policy goals. Although both countries show considerable progresses in the implementation of the UN strategies, it is doubtful that the existing global monitoring approach is appropriately designed for measuring the real situation on the ground. Our paper is based on a desktop research combined with stakeholder workshops and expert interviews. Overall, both countries made considerable progress in regard to disaster preparedness and monitoring. However, multi-risks are rarely considered and there is still increasing vulnerability due to the expansion of informal settlements. The risk management is characterized by an imbalanced distribution of financial resources and institutional capacities between the metropolitan regions and smaller municipalities, and by low public participation and hardly community-based approaches. The paper underlines the importance for more qualitative, in-depth studies on the root causes of disaster risk which could complement the global monitoring which is very much focused on quantitative data and shows inconsistency between input and output indicators.

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

scholarly journals The Hyperloop System and Stakeholders: A Review and Future Directions

2021 ◽  
Vol 13 (15) ◽  
pp. 8430
Author(s):  
Lambros Mitropoulos ◽  
Annie Kortsari ◽  
Alexandros Koliatos ◽  
Georgia Ayfantopoulou
Keyword(s):  
Literature Review ◽  
Online Search ◽  
Future Directions ◽  
Transport Mode ◽  
High Speeds ◽  
Current State ◽  
Comprehensive Literature Review ◽  
Planning Processes ◽  
Holistic Assessment ◽  
The Eu

The hyperloop is an innovative land transport mode for passengers and freight that travels at ultra-high speeds. Lately, different stakeholders have been engaged in the research and development of hyperloop components. The novelty of the hyperloop necessitates certain directions to be followed toward the development and testing of its technological components as well the formation of regulations and planning processes. In this paper, we conduct a comprehensive literature review of hyperloop publications to record the current state of progress of hyperloop components, including the pod, the infrastructure, and the communication system, and identify involved EU stakeholders. Blending this information results in future directions. An online search of English-based publications was performed to finally consider 107 studies on the hyperloop and identify 81 stakeholders in the EU. The analysis shows that the hyperloop-related activities are almost equally distributed between Europe (39%) and Asia (38%), and the majority of EU stakeholders are located in Spain (26%) and Germany (20%), work on the traction of the pod (37%) and the tube (28%), and study impacts including safety (35%), energy (33%), and cost (30%). Existing tube systems and testing facilities for the hyperloop lack full-scale tracks, which creates a hurdle for the testing and development of the hyperloop system. The presented analysis and findings provide a holistic assessment of the hyperloop system and its stakeholders and suggest future directions to develop a successful transport system.

scholarly journals How to Improve Effectiveness of Renewable Space Heating Programs by Better Understanding Homeowner—Installer Interactions

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4625
Author(s):  
Alisa Freyre ◽  
Stefano Cozza ◽  
Matthias Rüetschi ◽  
Meinrad Bürer ◽  
Marlyne Sahakian ◽  
...  
Keyword(s):  
Literature Review ◽  
Financial Incentives ◽  
Heating System ◽  
Heat Pumps ◽  
Single Family ◽  
Heating Systems ◽  
Current State ◽  
Ex Post ◽  
Improved Technology

In this paper, we perform a literature review on the current state of knowledge about homeowners in the context of the adoption of renewable heating systems. Despite a considerable number of studies about homeowners, homeowner–installer interactions, and ways to improve the effectiveness of renewable heating programs, based on homeowner knowledge, have not yet been studied in much detail. To address these knowledge gaps, we conduct a qualitative study on single-family house owners who installed heat pumps and took part in a renewable heating program in Geneva, Switzerland. We cover homeowner practices in choosing installers and heating system types, homeowners’ feedback about heat pump installation and use, as well as their experience in participation in the renewable heating program. Based on the literature review and the findings from the interviews, we provide the following recommendations on how to increase the effectiveness of renewable heating programs: (a) support for homeowners should not be limited to financial incentives; (b) partnership programs with installers could help to increase the quality of installation services and enable homeowners to choose qualified installers; and (c) assisting homeowners in pre-qualification and ex-post analysis, in learning how to operate their renewable heating systems and in solving problems during the post-installation period, can contribute to improved technology reputation, which can, in turn, increase technology uptake by other homeowners.

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