Advances in Risk Analysis with Big Data

Choi, Tsan‐Ming; Lambert, James H.

doi:10.1111/risa.12859

Cited by 79 publications

(46 citation statements)

References 28 publications

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“…It also helps to control both the operational and integrated risk. Choi and Lambert [13] stated that 'Big data are becoming more important for risk analysis' . It influences risk management by enhancing the quality of models, especially using the application and behavior scorecards.…”

Section: Big Data In Managing Financial Servicesmentioning

confidence: 99%

Current landscape and influence of big data on finance

2020

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In the age of technological innovation, various types of data are available with the advance of information technologies, and data is seen as one of the most valuable commodities in managing automation systems [13, 68]. In this sense, financial markets and technological evolution have become related to every human activity in the past few decades. Big data technology has become an integral part of the financial services industry and will continue to drive future innovation [12]. Financial innovations are also considered the fastest emerging issues in financial services. More specifically, they cover a variety of financial businesses such as online peer-to-peer lending, crowd-funding platforms, SME finance, wealth management and asset management platforms, trading management, crypto-currency, money/remittance transfer, mobile payments platforms, and so on. All of these services create thousands of pieces of data every day. Therefore,

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Section: Big Data In Managing Financial Servicesmentioning

confidence: 99%

Current landscape and influence of big data on finance

2020

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“…Nevertheless, data analytics for improved decision making has considerable implications for the field of risk analysis. (See, e.g., the August 2017 Special Issue of Risk Analysis , i.e., Choi & Lambert, .) The implications for resilience are less well investigated.…”

Section: The Allure Of Resilience Analyticsmentioning

confidence: 99%

Rethinking Resilience Analytics

2019

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The concept of "resilience analytics" has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data-driven analytic techniques, as well as the prevalence of high-profile natural and man-made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber-physical-social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamental surprise.

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“…To adapt the current cyber security standards, firstly the specific IoT cyber risk vectors need to be identified. By risk vectors, we refer to Internet of Things (IoT) attack vectors from particular approach used, to exploit big data vulnerabilities (Choi and Lambert, 2017).…”

Section: Introductionmentioning

confidence: 99%

Future developments in standardisation of cyber risk in the Internet of Things (IoT)

et al. 2020

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In this research article, we explore the use of a design process for adapting existing cyber risk assessment standards to allow the calculation of economic impact from IoT cyber risk. The paper presents a new model that includes a design process with new risk assessment vectors, specific for IoT cyber risk. To design new risk assessment vectors for IoT, the study applied a range of methodologies, including literature review, empirical study and comparative study, followed by theoretical analysis and grounded theory. An epistemological framework emerges from applying the constructivist grounded theory methodology to draw on knowledge from existing cyber risk frameworks, models and methodologies. This framework presents the current gaps in cyber risk standards and policies, and defines the design principles of future cyber risk impact assessment. The core contribution of the article therefore, being the presentation of a new model for impact assessment of IoT cyber risk. Keywords: Cyber risk; Internet of Things cyber risk; Internet of Things risk vectors;Standardisation of cyber risk assessment; Economic impact assessment. University of OxfordUniversity of Oxford 3 analysis to uncover the best method to define a unified cyber risk assessment. In section 7 we propose a new epistemological framework for cyber risk assessment standardisation and we discuss the new impact assessment principles. In Section 8 we present the conclusions and limitations of the research. METHODOLOGYThe methods applied in this study consist of literature review, comparative study, empirical analysis, theoretical and epistemological analysis and case study workshops. The selection of methodologies is based on their flexibility to be applied simultaneously to analyse the same research topic from different perspectives. We use practical studies of major projects in the I4.0 to showcase recent developments of IoT systems in the context of I4.0 high-tech strategies. We need practical studies to bridge the gaps, to assess the impact and overcome some of the cyber risk limitations and to construct the relationship between IoT and high-tech strategies. The proposed design principles support the process of building a holistic IoT cyber risk impact assessment model. Theoretical analysisThe methodology applies theoretical analysis through logical discourse of knowledge, also known as epistemological analysis. An epistemological analysis enables an investigation on how existing knowledge is justified and what makes justified beliefs justified (Steup, 2005), what does it mean to say that we understand something (Wenning, 2009) and how do we understand that we understand.The methodology reported here has two objectives. The first objective is to enable an up-todate overview of existing and emerging cyber risk vectors from IoT advancements, which includes cyber-physical systems, the industrial Internet of things, cloud computing and cognitive computing (MEICA, 2015;Weyer et al., 2015;Liao et al., 2017). If we were performing a vector specific analysis of r...

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Advances in Risk Analysis with Big Data

Cited by 79 publications

References 28 publications

Current landscape and influence of big data on finance

Current landscape and influence of big data on finance

Rethinking Resilience Analytics

Future developments in standardisation of cyber risk in the Internet of Things (IoT)

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