Predicting Business Bankruptcy

2016 ◽  
Vol 5 (2) ◽  
pp. 48-65
Author(s):  
Rui Sarmento ◽  
Luís Trigo ◽  
Liliana Fonseca
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Financial Institutions ◽  
Broad Spectrum ◽  
Financial Distress ◽  
Sustainability Assessment ◽  
Business Environment ◽  
Decision Makers ◽  
Government Agencies

Managers, investors, financial institutions and government agencies have a major concern on forecasting enterprise bankruptcy. It enables the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Throughout the 20th and the 21st century, advances in statistics and computer science fields enabled the development of different trends in financial distress assessment that co-exist today. However, recent Data Mining (DM) techniques are regarded as being the most precise. IT expertise requirements in the constantly evolving DM field may have been a major obstacle to the adoption of these techniques by decision makers. Furthermore, DM software tools that are now widespread offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting the appropriate algorithm. Hence, the adoption of a good workflow method for data processing and analysis is critical for having fast and reliable results. This work presents an overview of the available bankruptcy techniques and provides a comprehensive case study exploring the latest Data Mining techniques.

A Comprehensive Workflow for Enhancing Business Bankruptcy Prediction

2018 ◽  
pp. 2135-2160
Author(s):  
Rui Sarmento ◽  
Luís Trigo ◽  
Liliana Fonseca
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Empirical Study ◽  
Sustainability Assessment ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Prediction Performance ◽  
Critical Area ◽  
Classifier Selection ◽  
Comprehensive Knowledge

Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature.

A Comprehensive Workflow for Enhancing Business Bankruptcy Prediction

2015 ◽  
pp. 216-238
Author(s):  
Rui Sarmento ◽  
Luís Trigo ◽  
Liliana Fonseca
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Empirical Study ◽  
Sustainability Assessment ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Prediction Performance ◽  
Critical Area ◽  
Classifier Selection ◽  
Comprehensive Knowledge

Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature.

scholarly journals Boundaries Between Research Ethics and Ethical Research Use in Artificial Intelligence Health Research

2021 ◽  
pp. 155626462110027
Author(s):  
Gabrielle Samuel ◽  
Jenn Chubb ◽  
Gemma Derrick
Keyword(s):  
Artificial Intelligence ◽  
Health Research ◽  
Decision Makers ◽  
Societal Impact ◽  
The Past ◽  
Research And Innovation ◽  
The United Kingdom ◽  
Responsible Research ◽  
Past Half

The governance of ethically acceptable research in higher education institutions has been under scrutiny over the past half a century. Concomitantly, recently, decision makers have required researchers to acknowledge the societal impact of their research, as well as anticipate and respond to ethical dimensions of this societal impact through responsible research and innovation principles. Using artificial intelligence population health research in the United Kingdom and Canada as a case study, we combine a mapping study of journal publications with 18 interviews with researchers to explore how the ethical dimensions associated with this societal impact are incorporated into research agendas. Researchers separated the ethical responsibility of their research with its societal impact. We discuss the implications for both researchers and actors across the Ethics Ecosystem.

scholarly journals The Integrated Hydropower Sustainability Assessment in Tajikistan: A Case Study of Rogun Hydropower Plant

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Zhao Xu ◽  
Yumin Niu ◽  
Yangze Liang ◽  
Zhigang Li ◽  
Atoev Iftikhor
Keyword(s):  
Sustainability Assessment ◽  
Current Knowledge ◽  
Business Environment ◽  
External Factors ◽  
Evaluation Framework ◽  
Assessment System ◽  
Hydropower Plant ◽  
Sustainable Performance ◽  
The Impact

Tajikistan has formulated the strategy to rejuvenate the country through hydropower. The Rogun hydropower plant (HPP) is designed as the highest hydropower station, while its sustainability is also questioned due to a lack of comprehensive sustainability evaluation. Considering that the external environment of Rogun HPP is complex and changeable, its sustainable performance will be fragile and inconstant. To comprehensively assess the sustainable performance, an integrated evaluation framework, covering the current and dynamic sustainable performance, is urgently established. Therefore, this paper firstly explored the hydropower sustainability assessment indicators which can conform to Tajikistan’s situations and further examined the current sustainable performance of Rogun HPP. The case study found that Rogun HPP’s current financial viability, involuntary resettlement, the measures to prevent corruption, and information disclosure were seriously deficient. The SWOT analysis indicated the external factors, such as the Belt and Road Initiative, improving business environment, and easing geopolitical disputes, can eliminate weaknesses and improve the sustainable performance of Rogun HPP. At the same time, tight fiscal allocations and economic downturns will have negative influences on the sustainable performance. The integrated evaluation tool established in this paper can not only evaluate the current sustainable performance but also consider the impact of external factors on sustainable performance from a dynamic perspective. This paper contributes to the current knowledge system by establishing the hydropower sustainability assessment system which is suitable for Tajikistan’s conditions. Moreover, the results are informative for the decision-makers to have a better understanding of Rogun HPP’s current strengths and weaknesses, valuable opportunities, and potential threats.

scholarly journals Thematic Dynamics of Internet of Things (IoT): Impact on Digital Personalized Healthcare (PHC)

2021 ◽  
Author(s):  
Bongs Lainjo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Successful Implementation ◽  
Daily Lives ◽  
Personalized Healthcare ◽  
Iot Platform ◽  
Iot Platforms ◽  
Almost All

Abstract Background: Information technology has continued to shape contemporary thematic trends. Advances in communication have impacted almost all themes ranging from education, engineering, healthcare, and many other aspects of our daily lives. Method: This paper attempts to review the different dynamics of the thematic IoT platforms. A select number of themes are extensively analyzed with emphasis on data mining (DM), personalized healthcare (PHC), and thematic trends of a select number of subjectively identified IoT-related publications over three years. In this paper, the number of IoT-related-publications is used as a proxy representing the number of apps. DM remains the trailblazer, serving as a theme with crosscutting qualities that drive artificial intelligence (AI), machine learning (ML), and data transformation. A case study in PHC illustrates the importance, complexity, productivity optimization, and nuances contributing to a successful IoT platform. Among the initial 99 IoT themes, 18 are extensively analyzed using the number of IoT publications to demonstrate a combination of different thematic dynamics, including subtleties that influence escalating IoT publication themes. Results: Based on findings amongst the 99 themes, the annual median IoT-related publications for all the themes over the four years were increasingly 5510, 8930, 11700, and 14800 for 2016, 2017, 2018, and 2019 respectively; indicating an upbeat prognosis of IoT dynamics. Conclusion: The vulnerabilities that come with the successful implementation of IoT systems are highlighted including the successes currently achieved by institutions promoting the benefits of IoT-related systems like the case study. Security continues to be an issue of significant importance.

scholarly journals ON ARTIFICIAL INTELLIGENCE TALENT CULTIVATION IN CHINA FROM CASE STUDY

2021 ◽  
Author(s):  
XIAO-LONG LI
Keyword(s):  
Artificial Intelligence ◽  
Case Analysis ◽  
Decision Makers ◽  
Research Centers ◽  
Incentive Mechanisms ◽  
Four Dimensions ◽  
Academic Conference ◽  
Similarities And Differences

Through the case analysis of Carnegie Mellon University in the field of artificial intelligence in America. the similarities and differences of the above university in artificial intelligence talent cultivation were obtained from four dimensions: length of learning and degree, enrollment requirements, staff force construction and the offered curriculum. The conclusion could support the suggestions and advice for domestic policymakers and decision makers as follows: to promulgate the document of artificial intelligence talent cultivation pertinently as the guideline; to promote the new institutes construction of artificial intelligence and update the research centers of artificial intelligence; to improve the supporting incentive mechanisms such as scholarships, competitions and academic conference grants for the students in the direction of artificial intelligence.

Selecting International Freight Forwarders: An Expert Systems Application

1993 ◽  
Vol 23 (3) ◽  
pp. 11-21 ◽  
Author(s):  
Aysegül Özsomer ◽  
Michel Mitri ◽  
S. Tamer Cavusgil
Keyword(s):  
Artificial Intelligence ◽  
Expert Systems ◽  
Competitive Advantage ◽  
Broad Spectrum ◽  
International Marketing ◽  
Decision Makers ◽  
Third Party ◽  
Foreign Markets ◽  
Freight Forwarder ◽  
Selection Of

The recent changes in the international forwarding environment have witnessed the emergence of “new forms” of forwarders incorporating a broad spectrum of services under one roof. Such total logistics companies are becoming a critical third party in obtaining a competitive advantage in foreign markets. Hence, the evaluation and selection of an international freight forwarder is no longer a simple operational decision but a strategic one. Presents and explains an expert systems tool to assist decision makers in selecting the freight forwarder which fits their needs best. The system, called FREIGHT, brings together international marketing, logistics and artificial intelligence knowledge.

Graph-Based Data Mining

2007 ◽  
pp. 291-307
Author(s):  
Wenyuan Li ◽  
Wee-Keong Ng ◽  
Kok-Leong Ong
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Graph Mining ◽  
The Other ◽  
Complex Data ◽  
Graph Problem ◽  
Data Graph ◽  
Theoretical Results

With the most expressive representation that is able to characterize the complex data, graph mining is an emerging and promising domain in data mining. Meanwhile, the graph has been well studied in a long history with many theoretical results from various foundational fields, such as mathematics, physics, and artificial intelligence. In this chapter, we systematically reviewed theories and techniques newly studied and proposed in these areas. Moreover, we focused on those approaches that are potentially valuable to graph-based data mining. These approaches provide the different perspectives and motivations for this new domain. To illustrate how the method from the other area contributes to graph-based data mining, we did a case study on a classic graph problem that can be widely applied in many application areas. Our results showed that the methods from foundational areas may contribute to graph-based data mining.

Multidimensional Model Design using Data Mining

2017 ◽  
Vol 13 (1) ◽  
pp. 1-35 ◽  
Author(s):  
Sandro Bimonte ◽  
Lucile Sautot ◽  
Ludovic Journaux ◽  
Bruno Faivre
Keyword(s):  
Data Mining ◽  
Rapid Prototyping ◽  
Data Warehouse ◽  
Decision Maker ◽  
Decision Makers ◽  
Model Design ◽  
Multidimensional Model ◽  
Mapping Process ◽  
Using Data

Designing and building a Data Warehouse (DW), and associated OLAP cubes, are long processes, during which decision-maker requirements play an important role. But decision-makers are not OLAP experts and can find it difficult to deal with the concepts behind DW and OLAP. To support DW design in this context, we propose: (i) a new rapid prototyping methodology, integrating two different DM algorithms, to define dimension hierarchies according to decision-maker knowledge; (ii) a complete UML Profile, to define a DW schema that integrates both the DM algorithms; (iii) a mapping process to transform multidimensional schemata according to the results of the DM algorithms; (iv) a tool implementing the proposed methodology; (v) a full validation, based on a real case study concerning bird biodiversity. In conclusion, we confirm the rapidity and efficacy of our methodology and tool in providing a multidimensional schema to satisfy decision-maker analytical needs.

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