In conclusion, big data has been transforming the way banks operate. 11 June 2021 8 9 2 Recently, we have been hearing about Big Data more and more often. However, they are facing significant challenges when contemplating making full use of such data. Hasan, M. M., Popp, J., & Olh, J. Cyber-attacks are an increasing threat to financial service providers, particularly in banking. Gana, N., M. Abdulhamid, S., & A. Ojeniyi, J. What had been a steamroller of global financial . The role of big data in the banking industry. (2020). From this perspective, although big data in the banking industry has the prospect of bringing high benefits, at the same time, the cost is quite remarkable, and there is no way to avoid such kind of mandatory costs. Authors' contributions:Mr.Morshadul Hasan has developed the concept, prepared the draft, Dr.Thi Le grossly modified the draft and edit the manuscript, Dr. Ariful Hoque supervise the draft, guiding the project, editing the language, revising the manuscript, commenting on the manuscript and so on. Heres What We Know. Big Data and Service Operations. Cabrera-Snchez, J. P., & Villarejo-Ramos, . F. (2020). These trends will capture the market with a significant amount of money, such as the value of data innovation in cognitive computing will reach nearly $18.6B. This study provides a broad overview of how banking operations have been influenced by the emergence of big data, which involves the enormous data captured and stored at unprecedented levels of volume, velocity, and variety (Cohen, 2018). Giacalone, M., & Scippacercola, S. (2016). Big Data Opportunities and Challenges: the Case of Banking Industry. Finance Big Data: Management , Analysis , and Applications. Wenzel, R., & Van Quaquebeke, N. (2018). In. Big data analytics for supply chain relationship in banking. Introduction 2. This study sets the deductive logic and trending trends relating to big data and banking operations perspectives (Lamba & Singh, 2017). Big data helps progress transparency, audit ability, and executive oversight of any enterprise's risk (Srivastava & Gopalkrishnan, 2015), thus improving their decision-making ability. We expanded our search with more keywords such as big data in finance, big data in financial risk management, big data and decision making, big data challenges in banking, the impact of big data on banks, big data acquisition in banking, big data, and risk management, fraud detection through risk management in banking, banking management, data application in bank, digital banking, data-driven baking, and so forth on the prespecified research search engine that mentioned in data collection stage as the number of articles relating to big data was not enough for a good qualitative article. Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2020). Big data has played an increasingly important role in the growth of banking or the financial services sector. Soltani Delgosha, M., Hajiheydari, N., & Fahimi, S. M. (2020). This also boosts customer's demand for increased competition, better products/service, new technology usages, technology diffusion, and other factors. This situation forces financial institutions to consider transforming the existing data into valuable knowledge for management and creating as much profit as possible for the enterprises (Ngo et al., 2020; Raman et al., 2018). Goel, P., Datta, A., & Sam Mannan, M. (2017). This study used Scopus and Web of Science databases; these are popular among researchers to select relevant articles. (2020). Turning information quality into firm performance in the big data economy. The views of various researchers, fellows, and others related to big data and banking activities have been collected and analyzed. Mohamed, N., & Al-Jaroodi, J. Over several decades, banks have continually adapted the latest technology innovations to redefine how customers interact with them. With the rapid development of data innovations, the banking industry has gradually strengthened its connection with big data sources, screened out useful information, integrated multi-channel data, and enriched customer profiles to achieve sustainable operations. Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Gai, K., Qiu, M., & Elnagdy, S. A. The Impact of Big Data on Banking Operations. Oguntimilehin A, & Ademola EO. It is also essential to prepare the IT expert team to prioritize the most critical parts of the network and network segmentation as a strategic policy. Bauder, R. A., & Khoshgoftaar, T. M. (2018a). Whatever there was no specified schedule for the data collection process for this study. That leads to the emergence of new products that better meet current requirements. For these reasons, the amount of information is boosting globally (see Figure 1). Professional services stay at the same level as process manufacturing at 8.2% of total revenue. As banking is the most crucial industry worldwide, millions of customer transaction histories are highly frequent every minute (Skyrius et al., 2018). The analysis provides development prospects by improving decision-making and responding to requests. Big data influences dramatically many aspects of the financial service and banking industry, including the financial market (Shen & Chen, 2018), internet credit service company (Zhang et al., 2015), internet finance (Yang et al., 2017), management, analysis, and applications (Y. (2016). This study tests the existing concepts and introduces a comprehensive understanding of the current research on big data based on the qualitative method. Keeping up with big data volume allows banks to process its' information faster with worth value and at the same time avoid different potential embarrassing scandals due to lack of data. Parashar, S. (2020). This study also highlights the advantage of customer segmentation, which allows banks to better target customers through relevant marketing activities tailored to customer needs, Mainly the focus was "big data customer segmentation" (link). The rapid development of digitalization contributes to the ever-growing global data sphere, [3] https://www.globenewswire.com/news-release/2020/03/18/2002786/0/en/Global-Big-Data-Market-Insights-2020-2025-Leading-Companies-Solutions-Use-Cases-Business-Cases-Infrastructure-Technology-Integration-Industry-Verticals-Regions-and-Countries.html. Article (PDF-2 MB) Advanced analytics is enabling superior performance in organizations willing to make the proper commitment: across all industries, companies that are more analytically driven realize financial growth three times higher than their less analytical competitors, according to McKinsey's Analytics Quotient (see sidebar). This study also presents a brief of existing literature on big data and banking research. Banks and other financial service companies use algorithms based on real-time transaction data to obtain more accurate and less intrusive fraud detection methods. Purpose The paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to. These are considered the recent banking operations changes that were not present in traditional banking with manual activities in very narrow areas. How banks fight back against cyberattacks. Current landscape and influence of big data on finance. Besides, big data allows banks to prevent unauthorized transactions by providing a safety and security level, which raises the security standard of the banking industry. Many data analysts are good at finding the causes of the problems that have occurred through data analysis and management, but they have not enough ability to discover unknown issues. (2014). (Amakobe, 2015; Hassani et al., 2018b; T. S. Mohamed, 2019). Ngo, J., Hwang, B. G., & Zhang, C. (2020). In contrast, the digitalized big data set is a valuable support tool to increase business decision-making ability. van der Gaast, W., & Begg, K. (2012). Why is Empowerment Important in Big Data Analytics? Spending pattern of customers 2. Therefore, to construct a professional analysis team, the banking industry still has to go a long way (Court et al., 2015; Skyrius et al., 2018). The main aspects of things based on certain present literature on this study's primary idea should be grasped directly to conduct this qualitative research. Big Data in Finance. The data of this research has been collected from secondary sources. Customer Segmentation and Profiling 4. Strength in numbers: How does data-driven decision-making affect firm performance? (2019). (2003) for the data collection process of qualitative research. Elucidation of big data analytics in banking: a four-stage Delphi study. The Roles of Security and Trust: Comparing Cloud Computing and Banking. (2018). Discrete manufacturing is the second biggest sector, which contributed to almost 11.3% of total revenue. In the disorderly mass of data, the big data information processing platform is employed to sensitively capture the signals of risks and opportunities, improve the efficiency of obtaining information, and serve the entire decision-making process. Big data is still in the exploratory stage of operating model in the banking industry. Big data in agriculture: Does the new oil lead to sustainability? Tick by tick data or nearly real-time information allows companies to be much more agile than their competitors (Mcafee & Brynjolfsson, 2012). Variety relates to banking operations by maintaining multiple types of banking data. The data are also measured on different scales or are qualitative. To achieve this banking industry's expected goal, it usually needs to make necessary cloud computing technology preparations, distributed computing technology, and redundant configuration technology. (2019). Siddiqui, A. Yu, S., & Guo, S. (2016b). As both the external and internal parties are related to the security issue, this study emphasizes that dealing with external cyberattacks, inner security awareness, and management vulnerabilities brought about by security risks have become a crucial part of the banking industry's development challenge. Sicular, S. (2013). It was just because the literature relating to big data and banking is not well established. Global information systems also help overcome differences in distance, time, language, and culture and cooperate effectively. Exploring big data driven innovation in the manufacturing sector: evidence from UK firms. Stay up to date with the biggest stories of the day with ANC's 'Dateline Philippines' (15 July 2023) | ABS-CBN News Channel, Philippines Sun, Y., Shi, Y., & Zhang, Z. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review*. Big data analysis for financial risk management. Besides, some web contents have also been collected and noted for important reading. Due to the lack of an organizational structure, management system, and other aspects compatible with data security, the banking industry is currently facing relatively severe data security risks (Bouveret, 2018; N. Gana et al., 2019). Big Data Opportunities for Accounting and Finance Practice and Research. In. Do fintech lenders penetrate areas that are underserved by traditional banks? However, the bank still cannot completely prevent network security incidents and impossible to ensure 100% security (Patterson, 2016). Funding: There is no funding for this research. In response to the banking security issues, the banking industry must emphasize some core elements of the banking industry's data security system: transaction security, security compliance, network security technology, information security, the entire life cycle of data, etc. These are big data innovations, big data in retailing, big data in supply chain management, big data in management, big data in decision making, big data in industrial practice, big data and IoT, and so on to find qualitative findings from the related literature (Adam et al., 2014; Bradlow et al., 2017; Fisher & Raman, 2018; Lamba & Singh, 2017; Lee, 2018; Leskovec et al., 2015; Oguntimilehin A & Ademola EO, 2014). First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. How to navigate the challenges brought by big data is also a question that the banking industry needs to consider carefully (Bedeley & Iyer, 2014; Corporation, 2015; Hassani et al., 2018b; Hung et al., 2020). Its 4V characteristics comprise different challenges for management, analytics, finance, and so on other various applications. The use of big data will lead to more jobs, new standards and regulatory structures, etc. It also refers to managing structured, semi-structured, and unstructured data (Bedeley & Iyer, 2014; Chen, 2019). Promoting Chinas Inclusive Finance Through Digital Financial Services. Although commercial banks have increased their data security management investment in recent years, factors such as long business chains and complex software and hardware systems have further increased the hidden dangers of big data. Khade, A. The Practice of Innovating Research Methods. Banking with blockchain-ed big data. Internet Finance: Its Uncertain Legal Foundations and the Role of Big Data in Its Development. Also, big data and business analytics revenue are increased worldwide year to year. In, Mrquez, F. P. G., & Lev, B. Therefore, managing those expanded services makes sense in everyday banking operations. Research on Enterprise Credit System under the Background of Big Data. It is also vital to make the customers aware of information security awareness by educating them about online banking activities, dangers of phishing, ACH and wire fraud, malware, and more through different materials such as articles, posters, videos, email campaigns, newsletters. Access to useful and noiseless manual big data set is limited and costly, or even data might not be available in demand with appropriate digital format (Provost & Fawcett, 2013). Cohen (2018) mentioned that current clients' data could also attract similar new customers to enhance its market share. Based on these concepts, this qualitative study's methodology is followed by a structured research framework followed by the authors two previous studies, Hasan et al. Some technological applications such as data storage and management technology, data integration, processing and presentation technology, data analysis, and mining technology are used to conduct in-depth mining and research of daily banking data and discover potentially valuable information from massive customer information, financial product information, and financial transaction information. It also elaborates and interprets the risk analysis information comparability faster than the traditional finance systems. Ethics approval and consent to participate: Not applicable. Kumire, J. The analysis and processing of big data, especially unstructured data, still lack helpful software and hardware supports. The real-time data will also be considered a fundamental value proposition in every case, segment, and solution. Keyword Searching is one of the most important issues in the initial stage of a study. For management virtualization, various documents and vouchers in the banking business will appear in digital files in an electronic and data management model, which will continue to impact the traditional commercial bank operation model. The academicians widely use a structured research framework, researchers, university graduates, and so forth on their research and always stay up-to-date with the variations of a new qualitative research framework structure (Molasso, 2006). As big data analytics is not an old concept, and its considered the concept of this decade, contents from the webpage also helped extend the discussion in different viewpoints.

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