Business intelligence in risk management: Some recent progresses

Wu, Desheng Dash; Chen, Shu‐Heng; Olson, David L.

doi:10.1016/j.ins.2013.10.008

Cited by 133 publications

(63 citation statements)

References 51 publications

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“…Under such circumstances, a challenge for policy makers and health managers is how well the health service will be able to respond to sudden extreme changes in climate factors. Wu et al (2014) suggest that risks may be studied from a multidisciplinary perspective (e.g. financial risk, environmental risk due to human endeavours) and one of the effective ways to cope with risks is to adopt an integrated approach specific to any area or organisation (Wu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Towards a threshold climate for emergency lower respiratory hospital admissions

Islam

Chaussalet

Koizumi

2017

Environmental Research

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Identification of 'cut-points' or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated 2 a 'Climate Threshold' for emergency hospital admissions of chronic lower respiratory diseases by using a distributed lag non-linear model (DLNM). We analysed a unique longitudinal dataset relative humidity (≤ 40%), high Pm10 level (≥70-µg/m 3 ), low wind speed (≤ 2 knots) and high rainfall (≥30mm). Beyond the threshold values, a significantly higher number of emergency admissions due to lower respiratory problems would be expected within the following 2-3 days after the climate shift in the Greater London. The approach will be useful to initiate 'region and disease specific' climate mitigation plans. It will help identify spatial hot spots and the most sensitive areas and population due to climate change, and will eventually lead towards a diversified health warning system tailored to specific climate zones and populations.

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Section: Introductionmentioning

confidence: 99%

Towards a threshold climate for emergency lower respiratory hospital admissions

Islam

Chaussalet

Koizumi

2017

Environmental Research

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“…Business intelligence can help managers improve the quality of decision-making process and take adequate strategy to enhance an enterprise's competitiveness and profitability by transforming internal and external data (both structured and unstructured) into useful information [3].…”

Section: A Business Intelligencementioning

confidence: 99%

Network Security Analysis by Using Business Intelligence

Chen¹,

Li²,

Hsiao³

et al. 2015

IJMLC

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Abstract-Network devices including firewall, QoS, Anti-Virus wall, IDS and so on were developed to help administrators monitor internet usage behaviors in response to network applications. However, it requires administrators to log on different network devices for acquiring associated usage logs for further respective analysis whenever abnormal network usage behavior occurred. It is both difficult and time consuming for administrators to manage usage logs from all the devices within a network. Therefore, how to provide administrators integrated information through one single platform for more effective management and efficient data inquiry is the aim of this research. This study proposed the measure that the usage logs from network devices can be stored in the data warehouse where the necessary information within a specific timeframe was acquired by business intelligence system for further comparison and integration. It can save the time for log inquiries and assist efficient network users behavior analysis.

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“…ERM prescribes the development and execution of integrated strategies and processes to anticipate, face and overcome risks (Wu, Chen, & Olson, 2014;Wu, Olson, & Dolgui, 2015). ERM subdomains include investment risk evaluation (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Wu, Zhang, Wu, & Olson, 2010). Risk management often relies upon business intelligence nowadays, and more specifically, data mining (Wu, Chen, et al, 2014). In this context, this paper focuses on models for business failure prediction (BFP) that are widely used as early warning systems for financial distress or bankruptcy in partnering companies.…”

Section: Introductionmentioning

confidence: 99%

The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles

Bock

2017

Expert Systems with Applications

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International audienceNumerous organizations and companies rely upon business failure prediction to assess and minimize the risk of initiating business relationships with partners, clients, debtors or suppliers. Advances in research on business failure prediction have been largely dominated by algorithmic development and comparisons led by a focus on improvements in model accuracy. In this context, ensemble learning has recently emerged as a class of particularly well-performing methods, albeit often at the expense of increased model complexity. However, in practice, model choice is rarely based on predictive performance alone. Models should be comprehensible and justifiable to assess their compliance with common sense and business logic, and guarantee their acceptance throughout the organization. A promising ensemble classification algorithm that has been shown to reconcile performance and comprehensibility are rule ensembles. In this study, an extension entitled spline-rule ensembles is introduced and validated in the domain of business failure prediction. Spline-rule ensemble complement rules and linear terms found in conventional rule ensembles with smooth functions with the aim of better accommodating nonlinear simple effects of individual features on business failure. Experiments on a large selection of 21 datasets of European companies in various sectors and countries (i) demonstrate superior predictive performance of spline-rule ensembles over a set of well-established yet powerful benchmark methods, (ii) show the superiority of spline-rule ensembles over conventional rule ensembles and thus demonstrate the value of the incorporation of smoothing splines, (iii) investigate the impact of alternative term regularization procedures and (iv) illustrate the comprehensibility of the resulting models through a case study. In particular, the ability of the technique to reveal the extent and the way in which predictors impact business failure, and if and how variables interact, are exemplified

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Business intelligence in risk management: Some recent progresses

Cited by 133 publications

References 51 publications

Towards a threshold climate for emergency lower respiratory hospital admissions

Towards a threshold climate for emergency lower respiratory hospital admissions

Network Security Analysis by Using Business Intelligence

The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles

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