Big Data and Actuarial Science

Hassani, Hossein; Unger, Stephan; Beneki, Christina

doi:10.3390/bdcc4040040

Cited by 13 publications

(6 citation statements)

References 87 publications

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“…Most studies do not use particular theories. To identify and analyze the implementation challenges, researchers apply Delphi methods (4) and surveys (2). While a number of the sustainability and performance studies built on dynamic capabilities view and organizational information processing theory, studies on implementation challenges utilized a resource-based view, diffusion of innovation theory (DOI), a technology-organization-environment (TOE) framework, and others.…”

Section: Implementation Challengesmentioning

confidence: 99%

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Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

Lee

Mangalaraj

2022

BDCC

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Big data analytics has been successfully used for various business functions, such as accounting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In light of the great interest and evolving nature of big data analytics in supply chains, this study conducts a systematic review of existing studies in big data analytics. This study presents a framework of a systematic literature review from interdisciplinary perspectives. From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. Then, from the technical perspective, this study analyzes types of big data analytics, techniques, algorithms, and features developed for enhanced supply chain functions. Finally, this study identifies the research gap and suggests future research directions.

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Section: Implementation Challengesmentioning

confidence: 99%

“…BDA has been successfully used in core business functions, such as accounting, marketing, supply chain, and operations. For example, insurance companies have been using BDA and data mining to conduct more accurate policy pricing by utilizing a variety of big data available at multiple sources [2].…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

Lee

Mangalaraj

2022

BDCC

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“…Emerging technologies such as blockchain present secure mechanisms for creating, transferring and storing data (OECD, 2018). The analysis of large complex data sets, commonly referred to as 'big data', enables financial services organisations to model consumer-risk profiles accurately, thus providing financial products that are tailored to address the consumer's needs (Hassani, Unger, & Beneki, 2020). Through interconnected 'smart technological devices', the internet of things (IOT) enables financial organisations to generate big data (OECD, 2018).…”

Section: Introductionmentioning

confidence: 99%

Determinants of emerging technologies adoption in the South African financial sector

Matsepe

Lingen

2022

SAJBM

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The fourth industrial revolution (4IR) enables firms to leverage various emerging technologies to reduce operating costs, improve business efficiencies and gain competitive advantage. This article uncovers the determinants influencing emerging technology adoption, particularly artificial intelligence (AI), cloud computing and distributed ledger technologies (DLT), in South African (SA) financial services firms.Design/methodology/approach: Seventeen technology experts from the SA banking, insurance, financial technology and financial regulation and compliance sectors were interviewed. A semi-structured interview was used to conduct one-on-one interviews, followed by a focus group interview. Qualitative data were analysed using a thematic network analysis. Findings/results:The results revealed that the determinants -adopter traits, technology usability, industry characteristics, organisational leadership and organisational characteristics -were influential towards technology adoption. It is suggested that the new model could be strengthened further by incorporating a new construct, leadership diversity, which had not been previously proposed in the literature.Practical implications: By understanding the influential adoption determinants, leaders can take bold, calculated risks in adopting AI, cloud computing and DLT. However, the importance, prior to adopting these technologies, of clearly understanding the need for them, and their business benefits is also emphasised.Originality/value: Research on the adoption of AI, cloud computing and DLT in the SA financial sector is limited. This article leverages the models of the diffusion of innovations (DOI), the technology-organisation-environment (TOE) and the technology readiness index (TRI) to propose a new model that illustrates technology adoption in the SA financial sector at individual and firm levels.

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“…They are often generated from models applied to vocational pension schemes that cover groups of employees and are based on demographic and economic factors. Consequently, actuarial methods in connection with predictive analytics must significantly enhance their understanding of predicted behavior or events to support their policies and judgments [5]. In addition, implementing a year-by-year simulation approach to estimate the future costs of benefits is one of the most significant tasks for actuaries [6].…”

Section: Social Insurance Systemsmentioning

confidence: 99%

Using Fuzzy Time Series Models to Estimate the Cost of Benefits of Egyptian Social Insurance System

Raouf

Elsaieed

2022

IJFIS

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In recent years, many methods have been proposed to forecast data in different fields based on successful fuzzy time series models (FTS). Egyptian social insurance systems (SISs) need support to optimally define and estimate yearly total benefits (pensions), which helps the actuaries who are responsible for the system make optimal decisions. Given that the total benefits have not been forecasted before by prediction methods, this paper proposes FTS models by Chen, Cheng, Yu, and Song to forecast Egyptian social insurance benefits, proposes Huarng for appropriate partition lengths, and constructs the interval length using the difference in the transformation data method, given that the data has not been stationary in recent years and has increased significantly. The proposed approach is based on experiments implemented using four models with interval length partitions of 5, 10, 50, 100, and Huarng partitions of 465. The results show great progress in the performance of yearly benefit forecasting, especially in the Chen model with a Huarng 465 partition, which has high accuracy prediction with low error when training and testing data.

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Big Data and Actuarial Science

Cited by 13 publications

References 87 publications

Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

Determinants of emerging technologies adoption in the South African financial sector

Using Fuzzy Time Series Models to Estimate the Cost of Benefits of Egyptian Social Insurance System

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