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Insurance companies process much consumer data to innovate while aligning strategic organizational objectives to the evolving customer needs framework. Big data is a common denominator, from health, property, disability, automobile, to life insurance packages. With the current advancements in technology, consumers personalization and data privatization have become the firsthand priority. Therefore, insurance companies must leverage incoming advanced technologies. Such as artificial intelligence, blockchain technology, 5G, edge computing, cloud computing, and cyber security. To achieve an edge, edge computing, one of the most promising cutting technology in 2022, should be incorporated into the already crowded insurance market. Edge computing refers to the deployment of storage and computing resources at the data source, for example, within an insurers network infrastructure. Insurance companies employ large amounts of data to set premiums for premiums, which can be processed by the edge computing paradigms fast, secure and reliable infrastructure.
The focus for most insurance companies is obtaining sufficient data and acquiring excellent statistical analysis tools to process the data and get insight that can be used in decision making. Types of data currently at the forefront of revolutionizing insurance companies are categorized into; zero-party data, first-party data, second-party data, and third party data. Fifteen percent of all global brands collect and use zero-party data. It includes information about consumer interests, intents, and preferences, shared directly by customers to insurance companies.
First-party data is one the most salient strategic asset for insurance organizations. Zero-party data is easy to obtain and has little to no expense. Most importantly, it aligns with consumer privacy regulations. First-party information is collected from registered customers, submissions, transactions, marketing campaign activities, emails, purchase history, and web-behavior. Since the data collected is from registered users, the uses are limitless. Indeed, with cutting-edge technology such as edge computing, and superior statistical analysis tools, insights gathered could revolutionize the customer experience. Second-party data is collected with the help of partner companies, such as insurance partnering organizations. Third-party data is shared by organizations such as Google and includes information gathered from ads. This type of information must be purchased and licensed for use by the provider.
There is much data insurance companies use for whatever strategic organization objective a company aims at. With such large-scale data resting on their grasp, issues of bandwidth load, response speeds, security, and privacy emerge. Indeed, this is the sole function of edge computing. Edge computing gives insurance companies a competitive advantage since data access and retrieval are secure, fast, and efficient. Unlike cloud computing, edge computing allows the organization to analyze data quickly and form insights as the data is located in the company. With cloud computing, a request has to be made to the server, after which the data requested is transferred to the user. The submission and retrieval of information from remote serves are redundant and time-consuming. Moreover, problems such as bandwidth and latency inhibit the quick processing of customer data, which eventually reduces customer experience. These problems are no longer of concern with the adoption of edge computing as computing and storage resources are within the insurance companys network infrastructure.
Every industry has realized that data is the key to superior operations. Indeed, significant technological advancements such as blockchain technology, artificial intelligence, 5G, edge computing, and cloud computing are all based on distinct manipulation of data sets. Of importance in this study is the understanding of data, and tools that can be employed in manipulating the data, to improve existing insurance processes. Due to the nature of insurance operations, edge computing is the best cutting-edge technology that may revolutionize the entire industry.
Edge computing refers to the storage and processing of data within an institutions network rather than from the cloud. The insurance companies collect multiple data sets classified as zero-party, first-party, second-party, and third-party data. Data collected is significant as it enables the organizations to perform product development, premium pricing, distribution, and underwriting (Eckert & Osterrieder 360). Therefore, a suitable technology is necessary to store and process this data, which puts the organization at the forefront of its business operations (Shi & Schahram 81: Tadapaneni 15). On the other hand, insurance companies continuously seek better solutions to collected data. As such, edge computing provides this advantage and more in the storage and processing of insurance data.
Edge computing rests at the forefront of data storage, retrieval, and processing. Panchali (13) posit that it is a paradigm that runs computing functions at a networks edge. The entire idea of edge computing is to bring computing operations near the data source. There are varied definitions of the paradigm, often based on the objective of the computing process. Cao, et al.: Shi & Schahram (82) define it as a novel approach to network execution characterized by downlink and uplink data. Downlink data delineates cloud service, whereas uplink data exhibits the internet of everything. Further, edge of edge computing is a paradigm that supports network resources and arbitrary computing processes between the cloud computing center path and the data source (Cao et al.). The computing process deploys storage resources such as fog nodes, cloudlets, and microdata centers located at the edge of a network close to sensors or predefined peripheral devices.
Edge computing can be used in insurance companies to perform data generation and calculations at the networks end. Further, edge computing migrates the organizations computing resources, such as storage, to a node at the end of the network (Cao et al.). Moreover, Tadapaneni supports that edge controls numerous processors involved in the data processing. It occurs via agile linking, application intelligence, secure and private gateways, and data optimization capabilities at low latency and high bandwidth within the institutions network infrastructure.
Edge computing offers low latency speeds during data transmission sessions in insurance companies. The Center for Insurance Policy and research posits that insurance companies, particularly those offering automobile insurance premiums, require real-time data on drivers behavior. The real-time data on drivers is to provide personalized insurance packages and premium discounts (NAIC). Telematics technology, which involves monitoring cars, equipment, and trucks, among other assets using onboard diagnostics (OBD) and GPS, significantly supports the collection of real-time data on a target asset movement. The data transmitted to and from both terminals is often extensive, leading to performance and bandwidth issues. Edge computing solves this problem for insurance companies by reducing data transmission delays.
Security and privacy are the frontlines of insurance companies strategic objectives. Insurers are now dealing with big data to streamline organization processes. Big data is being used to facilitate multiple insurance institutions issues. These include enhanced claim capabilities, reduced fraud through novel identification methods, enhanced solvency by improving the ability to assess risk accurately, and tailoring processes to customer needs (NAIC). Despite these advantages, big data poses insurers with several challenges that edge computing solves. These include voluminous and complex data sets, inaccurate algorithms for data synthesis, and, most importantly, security for all this data. With edge computing, security is still a concern; however, the risk is minimized as the control of the network infrastructure rests in the hands of the insurer.
Due to the promises of low latency, reduced bandwidth costs, security, and privacy, companies are moving from traditional cloud computing and installing edge analytics. Traditional cloud provider platforms include Microsoft Azure, Amazon AWS, and Google cloud. Examples of companies in the frontline for providing edge technology include Samsung, Verizon, IBM, Amazon, ZTE, Tencent, Microsoft, Cisco, Huawei, and Intel. The input of all these corporations to create Edge technology will ensure it revolutionizes IT operations and transition it into the corporate mainstream.
Edge analytics presents an enormous market opportunity that is yet to be harnessed. In todays business world, clients require operational plans and strategies that harness the possibilities of actionable real-time insights. Caprolu (118) delineates that clients constantly seek to apply learning and predictive analytics in transforming assets and workflows while enabling regular digital transformations. By bringing data storage and computational power closer to the workplace, immediate insights can be deduced from the interconnected intelligent devices within an institution, helping the organization enhance operational responsiveness. According to IBM, at least three-quarters of organizations are investing in artificial intelligence, which allows the incorporation of edge analytics. Essentially, edge analytics presents a market segment where automation, edge device interconnectivity, and intelligent workflow will positively impact organization ROI.
Edge analytics and computing offer many technical functionalities that could generate new sources of revenue or improve on existing ones when well executed. One of the significant features of edge analytics presents data security (Lan et al. 3: Raj & Paliwal 116). Traditional cloud computing comprises numerous data leaks between the user and the server, offering a considerable cost burden in a constant loop. With edge analytics, an organizations security budget is cut by half (Sangamithra et al. 73). Similarly, edge analytics low latency and bandwidth features prevent extra expenses that would otherwise be used to improve these functions (Sittón-Candanedo et al. 294: Caprolu 123). Further, the technical benefits exhibited by edge computing can aid in introducing new products and services. Indeed, the organization can capitalize on the edge hype and utilize the improved technological advantage to enhance business branding and customer experience.
Edge computing rests at the forefront of data storage, retrieval, and processing. The computing paradigm brings essential computing resources such as storage and processing capabilities at the point of action, which is, near the data source, reducing latency and transmission speeds, reducing bandwidth, and improving security. Due to edge analytics powerful applications in data management, insurance companies zero-party, first party, second party, and third-party data can be easily handled. For instance, edge computing can be employed to perform calculations and generate insights from data in real-time, all at low latency rates and secure network infrastructure. By bringing the powerful computational processing resources near the data source, insurers can transform workflows and assets while improving operational responsiveness. Industries and organizations use big data to run most of their daily activities. Big data enhances privacy in hospitals and government archives that ensure secrecy in the necessary information. Insurance companies need to adopt big data to ensure that their privatization is on another level that no unauthorized person can access.
Works Cited
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Cao, Keyan, et.al. An Overview on Edge Computing Research . IEEE Access, vol. 8, 2020, pp. 8571428. Crossref, Web.
Caprolu, Maurantonio, et.al. Edge Computing Perspectives: Architectures, Technologies, and Open Security Issues . 2019 IEEE International Conference on Edge Computing (EDGE), 2019. Crossref, Web.
Eckert, Christian & Katrin Osterrieder. How digitalization affects insurance companies: overview and use cases of digital technologies . Zeitschrift für die gesamte Versicherungswissenschaft, vol. 109, nr. 5, 2020, pp. 33360. Crossref, Web.
Lan, Liyun, et.al. Overview of Enterprise IoT Security System based on Edge Computing . Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 2020. Crossref, Web.
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Shi, Weisong & Schahram Dustdar. The Promise of Edge Computing . Computer, vol. 49, nr. 5, 2016, pp. 7881. Crossref, Web.
Sittón-Candanedo, Inés, et.al. A review of edge computing reference architectures and a new global edge proposal . Future Generation Computer Systems, vol. 99, 2019, pp. 27894. Crossref, Web.
Tadapaneni, Narendra Rao. Overview and Opportunities of Edge Computing . SSRN Electronic Journal, 2016. Crossref, Web.
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