According to Figure 1, the last decade was the booming decade for data innovation. Goel, P., Datta, A., & Sam Mannan, M. (2017). To grasp customers' requirements from multiple angles and directions, breaking the traditional indifference, passive product and service marketing methods, and providing customers with a complete data structure are vital needs to attend. Big data and data analytics have become day-to-day activities of firms. Brynjolfsson, E., Hitt, L., & Kim, H. (2011). Impact of big data analytics on banking: a case study Check out how the pillars of big data relate to banking. These changes significantly affect habituating with traditional banking (M. M. Hasan, Popp, et al., 2020; Ram & Yang, 2017). (2019). Commercial banks must promote individualized services through active marketing for new customers and take the customer as the centre to prescribe the right medicine. 36 No. Fosso Wamba, S., Akter, S., & de Bourmont, M. (2019). The challenge is to transform the data into strategic levers to improve customer satisfaction and, thus, its performance by tuning the information quality and big data analytics (Fosso Wamba, Akter, & de Bourmont, 2019; Fosso Wamba, Akter, Trinchera, et al., 2019). Big data techniques in auditing research and practice: Current trends and future opportunities. The Impact of Big Data on Banking Operations - Europe PMC Global information is also snowballing, generating hundreds of billions of data every day (M. M. Hasan, Popp, et al., 2020). In. (2020). Methodology 4. Impact of big data analytics on banking sector: Learning for Indian Banks. Data mining technology has broad market prospects in decision support. The data of this research has been collected from secondary sources. Cerchiello, P., & Giudici, P. (2016). Big Data Opportunities and Challenges: the Case of Banking Industry. In the past, banks' risk management and decision-making were mainly based on subjective empirical judgments, supplemented by data support, resulting in poor risk management performance (Parashar, 2020). Turning information quality into firm performance in the big data economy. Hung, J. L., He, W., & Shen, J. Exploring big data driven innovation in the manufacturing sector: evidence from UK firms. Analysis of the current situation in commercial banks shows that there is a shortage of data analysts. Andrew Ginter. Three V's can be used to describe big data flows. Artificial Intelligence: In Banking A Mini-Review. Challenges and Solutions for Climate Change. (2020). Big Data and Service Operations. Evangelatos, N., Reumann, M., Lehrach, H., & Brand, A. Besides, training the internal parties regarding the high adaptability of technological devices is also very crucial. Banking Industry Faces Surge in Cyber Security Challenges. As big data is still a new and emerging research topic, where researchers are trying to establish some fundamental theorem, this study will provide the crucial concepts, which lead to the essence of big data in the banking industry. (2019b). Leskovec, J., Rajaraman, A., & Ullman, J. What had been a steamroller of global financial . Stay up to date with the biggest stories of the day with ANC's 'Dateline Philippines' (15 July 2023) | ABS-CBN News Channel, Philippines (2016). (2014). Big data in operations and supply chain management: current trends and future perspectives. The Impact of Big Data on Banking Operations - ResearchGate He, W., Hung, J.-L. and Liu, L. (2023), "Impact of big data analytics onbanking: a case study", Journal of Enterprise Information Management, Vol. Gepp, A., Linnenluecke, M. K., ONeill, T. J., & Smith, T. (2018). In the current technological advances with the rise of the information revolution through mobile internet, cloud computing, big data, and the Internet of Things (IoT), the banking industry is receiving new opportunities and facing critical challenges. A. Gana, N., M. Abdulhamid, S., & A. Ojeniyi, J. (2020), highlighting definite inclusion and exclusion processes. Background Of This Study 3. Muhammad, S. S., Dey, B. L., & Weerakkody, V. (2018). . The choice of technology for big data involves decision-making risks. However, the regulations, transformation, technological advancements, and innovations will not work unless incumbent institutions entrants a data-driven approach with high financial performance and more significant profits (Fosso Wamba et al., 2020; Glass & Callahan, 2014; Hale & Lopez, 2019; Hung et al., 2020; Mohiuddin et al., 2021). A research framework is essential to classify the qualitative research structure from the literature review data (Abdulla et al., 2019). Hassani, H., Huang, X., & Silva, E. (2018a). 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. Also, these two databases are the most accepted and well-known databases all over the world with more than 20000 journals in Scopus and 12000 journals in the web of science (Lamba & Singh, 2017). Database vulnerabilities, privacy breaches, and users' information leaking by internal employees have frequently occurred in companies (Bandara et al., 2020), more particularly in the banking industry. 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. In terms of data processing, commercial banks have accumulated much experience in the application of structured data. This 360-degree view operates like a crystal ball by showing the inside look of the past, present, and future customer-organization relationships that are the big data management object. You may be able to access teaching notes by logging in via your Emerald profile. Bandara, R., Fernando, M., & Akter, S. (2020). ICARE: A framework for big data-based banking customer analytics. The role of big data in the banking industry. van der Gaast, W., & Begg, K. (2012). Motammarri, S., Akter, S., Yanamandram, V., & Wamba, S. F. (2017). Agile visual analytics for banking cyber big data.. The Top 20 Cyberattacks on Industrial Control Systems. The 360-degree view works as groundwork, making the relationship between an organization's and customers experiential rather than transactional. Global Wealth Report 2023. Bedeley, R., & Iyer, L. S. (2014). This collection process focused not only on any specific area but also on big data in different business aspects. Every day's trillions of data provide numerous opportunities to the people, such as effective communication at a lower cost, using global information systems to work together from different places, making decisions, monitoring the transaction process, and providing control measures. In the current technological advances with the rise of the information revolution through mobile internet, cloud computing, big data, and the Internet of Things (IoT), the banking industry is. Hasan, M. M., Yajuan, L., & Khan, S. (2020). The data are also measured on different scales or are qualitative. Scammers can quickly exhaust personal financial accounts or steal thousands of dollars from credit cards. (2019). This research will have a significant implication in the banking industry that big data operation is critical for data-driven banking decisions. It also provides a company to extend the SMEs supplier payment terms to optimize business cash flow for physical materials, goods, and services (Chen, 2019). These challenges consist of effectively organizing and managing banking sectors, finding novel business models, handling traditional banking issues (Y. For instance, the French investment banking group BNP. (Amakobe, 2015; Hassani et al., 2018b; T. S. Mohamed, 2019). Sun et al., 2019). Commonly the literature reviewing helps most find out the gaps of the qualitative research and highlight the research boundary (Tranfield et al., 2003). Impact Factor: 2.032 5-Year Impact Factor: 2.100 JOURNAL HOMEPAGE SUBMIT PAPER Open access Research article First published online December 17, 2021 Big Data Applications the Banking Sector: A Bibliometric Analysis Approach Initially, this study used Scopus and Web of Science database as the main sources of the research search engine (M. M. Hasan, Yajuan, & Khan, 2020; M. M. Hasan, Yajuan, & Mahmud, 2020). 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). Some literature has been excluded particularly those articles dont focus on benefit and focus on big data and banking, as well as those publications dont focus on the real applications of big data on banking. 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. However, premature investment in selecting software and hardware that are not suitable for the bank's specific actual situation or too conservative inaction will hurt commercial banks' development (Balachandran & Prasad, 2017; Delgosha et al., 2019; Shamim et al., 2019). Zetzsche, D. A., Arner, D. W., Buckley, R. P., & Weber, R. H. (2019). Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. Besides, the product virtualization will also influence big data management, as different products will change their profit earning behaviour and be exchanged faster as various types of data signals. This is a preprint; it has not been peer reviewed by a journal. Cooperation can be improved through groupware software, group decision support systems, extranets, and electronic meeting facilities. The Impact of Big Data in Banking | Xtracta Sun, N., Morris, J. G., Xu, J., Zhu, X., & Xie, M. (2014). Bauder, R. A., & Khoshgoftaar, T. M. (2018b). Banking with blockchain-ed big data. Ram, J., & Yang, H. (2017). Thus, the critical claim applied to banking operations is creating solutions to unlock the vital information hidden in big data. 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). Cohen, M. C. (2018). 92 of the top 100 global banks still run their operations on IBM mainframes. It can be used for business management applications such as banking database marketing, banking customer segmentation, clients background analysis, customers credit scoring, fraud detection, and market analysis activities. This study used Scopus and Web of Science databases; these are popular among researchers to select relevant articles. This study also presents a brief of existing literature on big data and banking research. Tick by tick data or nearly real-time information allows companies to be much more agile than their competitors (Mcafee & Brynjolfsson, 2012). As the demand for data innovation increases daily, big data analytics software's revenue is also growing rapidly. Big Data in the Finance and Insurance Sectors. Therefore, competing with the comparatively larger firms sense more complicated than the earlier times in traditional banking. Identifying the solutions for these issues requires the total attendance of data scientists, marketers, lawyers, managers, and regulators. However, when we limited the searching elements within the article's title, we found only 39 articles (SCOPUS) and 20 (WoS) related to a different aspect of banking. They cannot obtain emotional or behavioural data of customers' hobbies, living habits, and consumption tendencies in social life. Thus, big data has influenced banking management, analysis, and applications for different products and services. The Impact Of Big Data On Businesses, Workforce and Society Big Data Approach and its applications in Various Fields: Review. Big data analytics in banking can be used to enhance your cybersecurity and reduce risks. The moves left BofA, the second-largest US bank by assets, with more than $100bn in paper losses at the end of the first quarter, according to data from the Federal Deposit Insurance Corporation . Banks further alerted about cyberattack threat. Most of the companies worldwide started exploiting the potential opportunities offered by utilizing big data. The Role of Big Data Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. (2020). Big Data , Analytics , and the Future of Marketing & Sales. Singha, R. (2020, December 19). In this study, we discuss how the presence of big data has been shaping the banking sector. Why must banks become AI-first? After initial screening, the targeted articles relating to the research field have been selected for further reading. How Research in Production and Operations Management May Evolve in the Era of Big Data. Shen, D., & Chen, S. (2018). How banks fight back against cyberattacks. In terms of velocity, big data involves data creation speed, which is even more critical than the volume for many applications. Yang, D., Chen, P., Shi, F., & Wen, C. (2017). (2013). In. Most commercial banks have not established a mature data analysis team with high sensitivity to data's value, professional ability, and data analysis experience. This research will have a significant implication in the banking industry that big data operation is critical for data-driven banking decisions. In this paper, the significant challenges are summarized, given in the banking sector's typical characteristics in the era of big data. (2014). 1 Introduction We board a learning driven world, the immediate after effect of approaches in data and corresponding advancements. Provost, F., & Fawcett, T. (2013). Second, a successful campaign also relies on technical factors. Zhang, S., Xiong, W., Ni, W., & Li, X. (2020). While online banking mainly focuses on digitizing the basic banking aspects such as money transfers, remote deposits, and bill pay, digital banking typically includes digitizing all the activities and programs performed by the different financial institutions, especially banks, and their customers. Big Data in Banking: Advantages and Challenges. These models, built from big data, also need to be constantly fed new input data, and this needs to happen faster than humanly possible (e.g., real-time analytics from news streams). The Impact of Big Data Analytics on the Banking Industry - ResearchGate Fosso Wamba, S., Queiroz, M. M., Wu, L., & Sivarajah, U. Sun et al., 2019; Yadegaridehkordi et al., 2020). Top 10 Big data Trends 2020. Still, premature investment in large amounts, selection of software and hardware that are not suitable for your actual situation, or too conservative inaction will have an adverse impact on the development of commercial banks (Balachandran & Prasad, 2017; Shamim et al., 2019; Soltani Delgosha et al., 2020). Banks are lucrative targets that the cybercriminals typically attempt to get internal access, damage, or alter the targeted network systems to steal funds and fundamental financial data such as bank account details, credit card information, and so on. 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. Over several decades, banks have continually adapted the latest technology innovations to redefine how customers interact with them. Funding: There is no funding for this research. Big data analytics-enabled sensing capability and organizational outcomes: assessing the mediating effects of business analytics culture. There are (a) identifying and categorizing the information assets such as confidential data, highly-restricted data, data for internal use, publicly used data based on the data value and level of sensitivity (b) implementing cybersecurity risk assessment process, (c) and educating the internal employees about the cybersecurity issues. In conclusion, big data has been transforming the way banks operate. As per a report by Research and Markets, big data in banking was valued at $7.19 billion in 2017 and is estimated to reach $14.83 billion by 2023, growing at a CAGR of 13% during the period. Volume refers to the enormous quantities of data that banks try to connect to improve data-based decision-making. Banks introduced ATMs in the 1960s and electronic, card-based payments in the '70s. Big data: Issues and an Overview in Some Strategic Sectors. The transformed data is processed to obtain valuable data information. Ngo, J., Hwang, B. G., & Zhang, C. (2020). Soltani Delgosha, M., Hajiheydari, N., & Fahimi, S. M. (2020). These peculiar features and specifications lead to the two forms of big data storage including structured data and unstructured data. (2020, January 28). Banks and other financial service companies use algorithms based on real-time transaction data to obtain more accurate and less intrusive fraud detection methods. Gai, K., Qiu, M., & Elnagdy, S. A. Analysis of Factors that Influence Customers Willingness to Leave Big Data Digital Footprints on Social Media: A Systematic Review of Literature. For example, attacks in the banking sector have shown an intensifying trend nowadays. Delgosha, M. S., Hajiheydari, N., & Fahimi, S. M. (2019). (2016). The real-time data will also be considered a fundamental value proposition in every case, segment, and solution. https://doi.org/10.1108/JEIM-05-2020-0176. Heres What We Know. Bauder, R. A., & Khoshgoftaar, T. M. (2018a). We then discuss various big data analytics strategies to overcome the respective computational and data challenges. Here's what to know about the role big data is playing in finance and within your local bank. Spending pattern of customers 2. Security Risk Analysis and Management in Banking Sector: A Case Study of a Selected Commercial Bank in Nigeria. Hassani, H., Huang, X., & Silva, E. (2018b). It has lowered the industry entry barriers, allowing non-financial institutions to enter the financial system more and use their technological advantages and blind spots in the regulatory approach to gain competitive advantages (Akter, Gunasekaran, et al., 2020). In. In the earlier periods, big data was considered insignificant for most firms; however, the value of big data is now commonly accepted (Gonzlez-Carrasco et al., 2019). The hackers continuously target banks in different forms such as ransomware, malicious outsourcing, compromised remote site, Stuxnet, ICS insider, IT insider, Trojan horses, worms, and denial-of-service (DDoS) attacks (Andrew Ginter, 2018; Kumire, 2020). Zhao, X., Yeung, K., Huang, Q., & Song, X. Impact of Big Data Analytics In Banking Sector May 15, 2022 | Banking, Data Management, Data privacy & Big Data Analytics, Enterprise Services, Global IT Risk Assessment, Industry | 0 comments The banking sector has always been data-driven. Yu, S., & Guo, S. (2016b). This trend will be continued as more firms are seeking to integrate more sources of information. Bose, R., (Robert) Luo, X., & Liu, Y. In. These are crucial for social and economic development (Guimaraes & Sato, 1996; M. M. Hasan et al., 2019b; van der Gaast & Begg, 2012; Weinberg, 1998). Journal of Enterprise Information Management, Article publication date: 23 November 2022. Managing different types of vulnerabilities is also a highly crucial issue for banks. As banking industries accelerate into the industrial internet era, the new generation of information technologies and applications such as big data, artificial intelligence, cloud computing, continue to deepen, and the process of digitization and industrial upgrading is accelerating (Adam et al., 2014; Asadi et al., 2017; Bose et al., 2013; OECD, 2020; Orun Kaya et al., 2019; Smith & Nobanee, 2020). Thats why mainly this study focused on Elsevier, Taylor & Francis, Springer, emerald, Wiley, Sage, Informs, and some other search engines to collect more relevant articles with much acceptance. Big data analytics is critical in modern operations management (OM). The rapid growth of massive data and the increasing maturity of big data technology have made it possible to manage risks in the banking industry based on big data analysis. Hasan M, Le T, Hoque A. Preprint from Research Square, 09 Jun 2021 DOI: 10.21203/rs.3.rs-573323 . In this era of big data in which various data are available with the advance of information technologies, every financial company gets a billion data points in a day. Advances in Risk Analysis with Big Data. Although commercial banks themselves have a large amount of customer data and transaction data, collecting and processing more data creates competition among the competitors because of getting the advantages of data innovations (Fosso Wamba et al., 2018; Lioutas & Charatsari, 2020; Nekmahmud & Rahman, 2018). Adam, K., Adam, M., Fakharaldien, I., Zain, J. M., & Majid, M. A. In the traditional sense, banks can only grasp customers' financial behaviours related to the banking business. The application and development of big data is a general trend. As big data and banking are not so long-standing, getting more than 5 to 10 years of data was somewhat difficult. Giacalone, M., & Scippacercola, S. (2016). Keyword Searching is one of the most important issues in the initial stage of a study. Big data concepts, theories, and applications. Cyber-attacks are an increasing threat to financial service providers, particularly in banking. The global market value of big data and business analytics was valued at $168.8 billion in 2018. Reshaping competitive advantages with analytics capabilities in service systems. Big Data Analytics in Operations Management - Choi - 2018 - Production The Role of Big Data and Predictive Analytics in Retailing. How big data impacts the finance and banking industries Big Data fraud detection using multiple medicare data sources. There are many frauds in the banking and financial services sectors, so the companies use improved and better fraud detection methods based on real-time analysis of big data (Amakobe, 2015; Delgosha et al., 2019). Hasan, M. M., Yajuan, L., & Mahmud, A. Court, D., Perrey, J., McGuire, T., Gordon, J., & Spilecke, D. (2015). This study has focused on the articles title, abstract, keywords, and the main body that were highly related to the topic (Hutzschenreuter et al., 2020). After collecting the raw articles, first of all, those articles were divided into two parts based on the concept of whether articles meet the objectives of this study or not. 1. In J. M. Cavanillas, E. Curry, & W. Wahlster (Eds.). Rabhi, L., Falih, N., Afraites, A., & Bouikhalene, B. Although big data brings several benefits, some drawbacks are in order. Full text links . L, J. K., & Schmid, T. (2020). The third one variety, which is the most interesting of the three Vs, that is, big data comprises the data's different natures (Sicular, 2013). The framework is given in Fig. 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. However, it is predicted to grow to nearly $274.3 billion by 2022, with a five-year CAGR (compound annual growth rate) of 13.2 per cent[1]. Dialani, P. (2020). Data-driven development methods will also have a disruptive impact on its future. Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view. Big Data Opportunities for Accounting and Finance Practice and Research. (2019). It is also evident that this transformation is only in its infancy. (2020). The keywords searching area was specified within the article title, abstract, and keywords were initially search with the keywords. Acceptance and use of big data techniques in services companies. (2019). The Impact of Big Data on Banking Operations. A Review of Big Data Management, Benefits and Challenges. Mohiuddin, M., Mahfuzur, B., Ashraful, R., Bidit, A., & Dey, L. (2021). 11 June 2021 8 9 2 Recently, we have been hearing about Big Data more and more often. https://doi.org/10.21203/rs.3.rs-573323/v1, This work is licensed under a CC BY 4.0 License, You are reading this latest preprint version. Big Data in Banking: The Role and Ways of Impact on the - DICEUS Mohamed, N., & Al-Jaroodi, J. Also, they have the problem of precisely predicting the value of big data and future data trends. Satish, L., & Yusof, N. (2017). This study also followed searching some keywords of other areas. Jagtiani, J., & Lemieux, C. (2018). (1996). The impact of big data on firm performance in hotel industry. Several scholars claimed to add two more Vs in the definition of big data, that is, variability and virality. Corbett, C. J. Read article for free, . Yadegaridehkordi, E., Nilashi, M., Shuib, L., Hairul Nizam Bin Md Nasir, M., Asadi, S., Samad, S., & Fatimah Awang, N. (2020). Discussion And Conclusion Declarations References Status: Posted Version 1 posted 09 Jun, 2021 You are reading this latest preprint version Taking stock of consumer returns: A review and classification of the literature. Big data analytics in operations and supply chain management. Whatever, initially, we had to collect literature in the study selection process. 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). A Review: Big Data Analytics for enhanced Customer Experiences with Crowd Sourcing. Big Data: The Management Revolution. Moreover, big data enables banks to gain insight into customers' consumption behaviour and patterns, thus simplifying their needs and needs9. Though capturing and storing data has been invested heavily, less than 0.5% of the collected data was analyzed or used (Cohen, 2018). 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. Managing consumer privacy concerns and defensive behaviours in the digital marketplace. Also, data driven services by the service provider is always expected by the customer in real time (Motammarri et al., 2017), that is why, the market is moving fast and sustaining the market dynamics, which ultimately supports banking operations' digital growth for a more extended period and toward better banking systems. Day by day, how banks address those vulnerabilities is getting important in every level of banking operation. AI in banking: Can banks meet the challenge? | McKinsey IoT-based risk management performance big data analysis is a useful instrument capable of predicting different SCF risks. Fosso Wamba, S., Akter, S., Trinchera, L., & De Bourmont, M. (2019). These technologies use large amounts of data generated from diverse sources at high speed. The study focuses on the various dimensions of big data in banking and finance activities. A. Big data is really the future of businesses. They have to utilize Big Data to its full potential to stay in line with their specific security protocols and requirements. Product Cross Selling based on the profiling to increase hit rate 5. 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. Balachandran, B. M., & Prasad, S. (2017). Customer demand analysis of the electronic commerce supply chain using Big Data. The Practice of Innovating Research Methods. It was just because the literature relating to big data and banking is not well established. Different comprehensive methods, analytical techniques, standards for describing and managing decisions are essential for banking success (Jonker et al., 2012). Examining Impacts of Big data Analysis on Consumer Finance: A case of China. Factor-based big data and predictive analytics capability assessment tool for the construction industry. The impressive explosion of data and the rapid development of new technology have significantly transformed business strategies and management through AI, IoT, Bigdata analytics, cloud, and blockchain (Akter, Michael, et al., 2020). "A 360-degree view of the customer is the concept of being able to view and analyses all of the data you have about every single customer in isolation, in one location"8. Data sampling approaches with severely imbalanced big data for medicare fraud detection. Generally, data from society and mobile sources are applied for forecasting and identification.
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