A Review of Risk Management in Construction: Opportunities for Improvement

Azevedo, Rogério Cabral de; Ensslin, Leonardo; Jungles, Antônio Edésio

doi:10.4236/me.2014.54036

Cited by 28 publications

(13 citation statements)

References 31 publications

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“…Web of Science and Scopus (Elsevier). We exclude from the bibliometric portfolio: books, book covers, duplicate works and outside the subject of this research (de Azevedo et al, 2014;Ensslin et al, 2017). No time filter was defined when querying the databases.…”

Section: The Databasementioning

confidence: 99%

Forecasting Financial Distress With Machine Learning – A Review

Duarte

Barboza

2020

FSRJ

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Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic.

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Section: The Databasementioning

confidence: 99%

Forecasting Financial Distress With Machine Learning – A Review

Duarte

Barboza

2020

FSRJ

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“…This work can also be considered as qualitative and quantitative, since, in the first step, there was a qualitative selection to identify articles of bibliographic portfolio; and, in the second step, when data were analyzed as the number of publications and authors citations [16] [20].…”

Section: Methodological Frameworkmentioning

confidence: 99%

Research Process for Selecting a Theoretical Framework and Bibliometric Analysis of a Theme: Illustration for the Management of Customer Service in a Bank

Ensslin¹,

Dutra²,

Ensslin³

et al. 2015

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Globalization has brought to institutions greater competitiveness and visibility to products and services, which means that consumers started to be more demanding and take advantage of the best references for establishing standards for their care. The banking industry fits in this context, and to remain competitive they seek new developed management procedures to better serve their customers. The objective of this study is to demonstrate to the theme fragment bank funding and resource loans management, with an emphasis on customer relationship, how to select a bibliographic portfolio and perform their bibliometrics, in order to highlight the most recognized scientific contributions on the subject. This work is characterized as exploratory-descriptive, of theoretical-illustrative nature, using ProKnow-C-Knowledge Development Process-Constructivist as an intervention tool. The bibliographic portfolio with 13 articles was generated by the development process; the Journal of Financial Economics showed the highest impact factor to scientific community at both bases, JCR and SJR. Bank, Risk and Lending Outcomes were the main appellant keywords and the most prominent authors were Hans Degryse with two articles in the portfolio, article of Bank Governance, Regulation and Risk Taking, written by Luc Laeven and highest scientific recognition of Ross Levine.

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“…As stated in [2], the effects that the risks offer to civil construction companies have error margins that are considered harmful for the execution of planned activities, which can lead to the interruption or even the ruin of the project.…”

Section: Introductionmentioning

confidence: 99%

The importance of risk management in civil engineering

Almeida¹,

Pinto²,

Pinheiro³

2021

IJAERS

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The civil construction industry needs of a proper project management inside of companies, the risks must be dimensioned in order to avoid any negative impacts to the success of the project. The present article consists of a bibliographic description that starts from understanding the relevant importance of risk management applied to projects in the construction industry. This study consists of a comparative bibliographic analysis, between the guide to the best practices of project management, and the study of authors on the importance of risk management in the construction industry. Projects in civil engineering are based on the success of their done objectives, which are linked to compliance with the schedule, cost, deadline and quality. Thus, risk management is essential for the project success, enabling the monitoring of these parameters. Risk management creates a wide vision of the project, and turns possible to identify risk situations to prepare control plans for the most impacts. Through these plans, it is possible to mitigate expenses, avoiding rework, waste and delays in activities, in addition to contributing to the improvement of quality management system for the services performed.

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A Review of Risk Management in Construction: Opportunities for Improvement

Cited by 28 publications

References 31 publications

Forecasting Financial Distress With Machine Learning – A Review

Forecasting Financial Distress With Machine Learning – A Review

Research Process for Selecting a Theoretical Framework and Bibliometric Analysis of a Theme: Illustration for the Management of Customer Service in a Bank

The importance of risk management in civil engineering

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