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BCBS 239 sets out a challenging standard for risk data processing and reporting. Any bank striving to comply with the principles will be keen to introspect how risk data is organized and what execution capabilities are at their disposal. With this in mind, the current paper advises banks on the growing number of solutions, tools and techniques that can be used to support risk data management frameworks under BCBS 239.

This paper, based on a survey with 29 major financial institutions, including G-SIBs and D-SIBs from diversified geographical regions such as North America, Europe and APAC, aims to advise banks and other financial services firms on what is needed to become ready and compliant with BCBS 239. This paper discusses best practice solutions for master data management, data lineage and end user implementations.

The primary conclusion of this paper is that banks should not treat BCBS 239 as yet another compliance exercise. The BCBS 239 principles constitute a driving force to restore viability and improve risk governance. In light of the new standards, banks can benefit from making significant progress towards risk data management transformation. This report argues that banks need to invest in a solution that empowers those who use the data to manage risk data. Thus, operational complexities are lifted and no data operations team is needed for proprietary coding of the data. Only then banks will stay abreast of the competition, while becoming fully compliant with the BCBS 239 principles.

As noted by Prorokowski (2014), “Increasingly zero accountability, imposed, leveraged omnipresent vast endeavors, yielding ongoing understanding […] of the impact of the global financial crisis on the ways data should be treated” sparked off international debates addressing the need for an effective solution to risk data management and reporting.

This paper discusses the forthcoming regulatory change that will have a significant impact on the banking industry. The Basel Committee on Banking Supervision published its Principles for effective risk data aggregation and risk reporting (BCBS239) in January last year. The document contains 11 principles that Global Systemically Important Banks (G-SIBs) will need to comply with by January 2016. The BCBS 239 principles are regarded as the least known components of the new regulatory reforms. As it transpires, the principles require many banks to undertake a significant amount of technical work and investments in IT infrastructure. Furthermore, BCBS 239 urges financial services firms to review their definitions of the completeness of risk data.

Government administrative data have enormous potential for public and individual benefit through improved educational and health services to citizens, medical research, environmental and climate interventions and better use of scarce energy resources. The purpose of this study (part of the Administrative Data Research Centre in England, ADRC-E) was to examine perspectives about the sharing, linking and re-use (secondary use) of government administrative data. This study seeks to establish an analytical understanding of risk with regard to administrative data.

This qualitative study focused on the secondary use of government administrative data by academic researchers. Data collection was through 44 semi-structured interviews plus one focus group, and was supported by documentary analysis and a literature review. The study draws on the views of expert data researchers, data providers, regulatory bodies, research funders, lobby groups, information practitioners and data subjects.

This study discusses the identification and management of risk in the use of government administrative data and presents a risk framework.

This study will have resonance with records managers, risk managers, data specialists, information policy and compliance managers, citizens groups that engage with data, as well as all those responsible for the creation and management of government administrative data.

First, this study identifies and categorizes the risks arising from the research use of government administrative data, based on policy, practice and experience of those involved. Second, it identifies mitigating risk management activities, linked to five key stakeholder communities, and it discusses the locus of responsibility for risk management actions. The conclusion presents the elements of a new risk framework to inform future actions by the government data community and enable researchers to exploit the power of administrative data for public good.

This study establishes an ontology-based framework for rework risk identification (RRI) by integrating heterogeneous data from the information flow of the prefabricated construction (PC) process. The main objective is to enhance the automation level of rework management and reduce the degree of reliance on human factors and manual operations.

The proposed framework comprises four levels aimed at managing dispersed rework risk knowledge and integrating heterogeneous data. The functionalities were realised through an integrated ontology that aligned the rework risk ontology with the PC ontology. The ontologies were developed and edited with Protégé. Ultimately, the potential benefit of the framework was validated through a case study and an expert questionnaire survey.

The framework is proven to effectively manage rework risk knowledge and can identify risk objects, clarify risk factors, determine risk events, and retrieve risk measures, thereby enabling the pre-identification of prefabricated rework risk (PRR) and improving the automation level. This study is meaningful and lays the foundation for the application of other computer methods in rework management research and practice in the future.

This research provides insights into the application of ontology to solve rework risk issues in the PC process and introduces a novel risk management method for future prefabricated project research and practice. The findings have significant theoretical value in terms of enriching the methods of risk assessment and control and the information management system of prefabricated projects.

Disasters continue to be most prevalent and severe for marginalised communities. To reach those furthest behind first, as the global community pledges in the 2030 Agenda, a critical assessment of equity in disaster risk governance is necessary. Yet, the understanding of factors that mediate the capacity of the governance processes to achieve equity ambitions is limited. This paper addresses this gap by proposing and testing a conceptual framework to assess equity in disaster risk governance.

The framework analyses the extent to which institutional relationships and data in risk governance support inclusion and diversity of voice and enable the equitable engagement of communities. The study applied the framework to key risk policies across governance levels in Brazil and Colombia.

The study finds that institutional awareness of cross-sectoral and -scalar coordination clearly exists. Yet, the engagement of actors further down the governance scale is framed reactively at all scales in both countries. The analysis of the risk data practices indicates that although data integration and sharing are key policy priorities, the policies frame the relations of disaster risk data actors as hierarchical, with data needs determined from the top down.

A key contribution of this framework is that its equity view results in a nuanced analysis, thus pointing to the differences between the two countries concerning the factors that mediate these challenges and providing specific entry points for strengthening equity in risk governance policies.

The purpose of this study is to propose a bearer service, which generates and maintains a “digital doppelgänger” for every financial contract in the form of a dynamic transaction document that is a standardised “data facility” automatically making important contract data from the transaction counterparties available to relevant authorities mandated by law to request and process such data. This would be achieved by sharing certain elements of the dynamic transaction document on a bearer service, based on a federation of distribution ledgers; such a quasi-simultaneous sharing of risk data becomes possible because the dynamic transaction document maintain a record of state in semi-real time, and this state can be verified by anybody with access to the distribution ledgers, also in semi-real time.

In this paper, the authors propose a novel, regular technology (RegTech) cum automated legal text approach for financial transaction as well as financial risk reporting that is based on cutting-edge distributed computing and decentralised data management technologies such as distributed ledger (Swanson, 2015), distributed storage (Arner et al., 2016; Chandra et al., 2013; Caron et al., 2014), algorithmic financial contract standards (Brammertz and Mendelowitz, 2014; Breymann and Mendelowitz, 2015; Braswell, 2016), automated legal text (Hazard and Haapio, 2017) and document engineering methods and techniques (Glushko and McGrath, 2005). This approach is equally inspired by the concept of the “bearer service” and its capacity to span over existing and future technological systems and substrates (Kavassalis et al., 2000; Clark, 1988).

The result is a transformation of supervisors’ capacity to monitor risk in the financial system based on data which preserve informational content of financial instruments at the most granular level, in combination with a mathematically robust time stamping approach using blockchain technology.

The RegTech approach has the potential to contain operational risk linked to inadequate handling of risk data and to rein in compliance cost of supervisory reporting.

The present RegTech approach to financial risk monitoring and supervisory reporting is the first integration of algorithmic financial data standards with blockchain functionality.

Building information modelling (BIM) is not without risk, as the greater reliance on information technology has associated technical risks. Thus, the purpose of this paper is to assess the perceptions of the users of BIM regarding the likelihood of emergence of technical risks which might influence the successful application of BIM, to facilitate the successful implementation of BIM in the construction industry.

The primary data were collected via a questionnaire to document the BIM risks, where 105 responses were recorded from constructors, consultants, cost consultants and other professionals from the UK construction industry. Subsequently, the analysis of the results was driven by univariate and inferential statistics (ANOVA) to assess the perception of risk emergence.

The study found the most likely technical risks that might emerge from BIM application. These risks are complexity of transferring modelling data from one program to another, lack of understanding of the BIM for the different software platforms, interoperability shortcomings, failure to discover errors in the model and risks of different software platforms.

The results will certainly intensify the discussion about BIM risks, risk allocation and all other aspects that are related to BIM contractual processes. Also, the compiled list of risks will help stakeholders in assessing financial implications that may result from BIM application.

Important technical risks have been identified in the application of BIM. This renders a new understanding of the risks that might influence the successful application of BIM. The respondents generally agreed on the importance of the following risks: “complexity of transferring modelling data between programs from one program to another”, “lack of understanding of the BIM for the different software platforms”, “interoperability shortcomings”, “failure to discover errors in the model” and “risks of different software platforms”, which are in line with current literature.

The purpose of this paper is to investigate the implications for supply chain risk management (SCRM) by applying internet of things (IoT). Therefore, the impact and effects on the SCRM process, as well as the internal and external pathway and the outcome of SCRM are examined.

This study adopts a multiple case study methodology with twelve companies from the manufacturing industry. This study is guided by the information processing theory (IPT) and a theory-grounded research framework to provide insights into information requirements and information processing capabilities for IoT-supported SCRM.

The studied cases demonstrate an increase in data availability in the companies that contribute to improved process transparency and process management. Furthermore, the process steps, risk transparency, risk knowledge and risk strategies have been enhanced, which enabled improved SCRM performance by fitting information requirements and information processing capabilities, thus allowing for competitive advantage.

This study offers in-depth insights for SCRM managers into the structure of IoT systems, primary use cases and changes for the process itself. Furthermore, implications for employees, incentives and barriers are identified, which could be used to redesign SCRM.

This study addresses the requirement for additional empirical research on technology-enhanced SCRM, supported by IPT as a theoretical foundation. The radical change of SCRM by IoT is demonstrated while discussing the human role, implications for SCRM strategies and identifying relevant topics for future development.

Few of the established risk identification methods refer to low-severity yet high-frequency safety risks data that may lead to several safety risks being ignored, thus reducing the potential of learning from a considerable number of cases. The purpose of this study is to explore a new valid method based on preaccident safety supervision data to identify these minor construction safety risks during routine construction operations.

A total of 329 official construction safety supervision reports containing 5,159 safety problem records from Shanghai between 2016 and 2018 served as raw material for in-depth analysis. Given the characteristics of the data collected, text mining integrated with natural language processing was applied to review the supervision reports and group safety risks automatically.

This study clarifies the way in which the supervision data should be employed to analyze high-frequency–low-severity safety risks. From these data, seven unsafe-act-related and nine unsafe-condition-related risks are identified. Regarding unsafe-act-related risks, inappropriate human behaviors could usually occur in personnel management, contract management, expense management, material management and acceptance work. For unsafe-condition-related risks, hoisting, scaffolding and reinforcement works are the main generators of onsite safety hazards during construction operations.

The study includes implications for project managers and supervisors to facilitate more effective proactive risk management by paying more attention to collecting and employing the supervision data established in each routine inspection.

Whereas previous research focused on analyzing severe accidents, this study seeks to identify the high-frequency–low-severity construction safety risks using the preaccident supervision data. The findings could provide a new thought and research direction for construction safety risk management.

The purpose of this paper is to discuss how, despite increasing data availability from a wide range of sources unlocks unprecedented opportunities for disaster risk reduction, data interoperability remains a challenge due to a number of barriers. As a first step to enhancing data interoperability for disaster risk reduction is to identify major barriers, this paper presents a case study on data interoperability in disaster risk reduction in Europe, linking current barriers to the regional initiative of the European Science and Technology Advisory Group.

In support of Priority 2 (“Strengthening disaster risk governance to manage disaster risk”) of the Sendai Framework and SDG17 (“Partnerships for the goals”), this paper presents a case study on barriers to data interoperability in Europe based on a series of reviews, surveys and interviews with National Sendai Focal Points and stakeholders in science and research, governmental agencies, non-governmental organizations and industry.

For a number of European countries, there remains a clear imbalance between long-term disaster risk reduction and short-term preparation and the dominant role of emergency relief, response and recovery, pointing to the potential of investments in ex ante measures with better inclusion and exploitation of data.

Modern society is facing a digital revolution. As highlighted by the International Council of Science and the Committee on Data for Science and Technology, digital technology offers profound opportunities for science to discover unsuspected patterns and relationships in nature and society, on scales from the molecular to the cosmic, from local health systems to global sustainability. It has created the potential for disciplines of science to synergize into a holistic understanding of the complex challenges currently confronting humanity; the Sustainable Development Goals are a direct reflectance of this. Interdisciplinary is obtained with integration of data across relevant disciplines. However, a barrier to realization and exploitation of this potential arises from the incompatible data standards and nomenclatures used in different disciplines. Although the problem has been addressed by several initiatives, the following challenge still remains: to make online data integration a routine.