Use of Big Data in the Banking Industry

Use of Big Data in the Banking Industry
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Part 1
Following the 21st-century big data revolution, the banking sector has found resonance in big data analytics concerning the valuable information they have been safeguarding for decades. The use of big data in banking firms has unlocked enigmas of money flows and assisted in curtailing theft and other major disasters coined by a shift in customer behavior. The use of big data analytics has assisted in process scoping, which demands reporting, auditing, and firm compliance verification. Within this base, banks have realized a decline in operating and overhead costs. Furthermore, banks have created customer profiles that harbor gaps between clients and banking facilities. In this context, aspects such as verification, auditing, reporting, customer-related fraud, and security tenets have benefitted from big data. These aspects are the primary beneficiaries since big data analytics have made it easy to detect fraud since the customer profiles enable banks to keep track of individual transactions (Doerr, Gambacorta & Garralda, 2021, pp. 930). The primary shift brought by big data analysis is the availability of information needed and easiness of retrieving it.
Banking firms primarily focus on structured data since they mostly deal with financial records and transaction activities. At times, they use unstructured data such as multimedia files to ascertain customer expenditures, media, behavior, and demographic information (Zhang et al., 2020, pp.7). The data is always organized because it is targeted to specific aspects such as risk assessment, customer relations, supportive decision-making, and research on new ventures. They have to organize it since they deal with various data types, such as transaction credit scores and troves of risk assessments. Another reason it is organized is the velocity (banks receive lots of transactions within sixty seconds). The data is obtained from customer spending discoveries, primary channel transactions, profile segmentation, cross-selling of assets, control and fraud prevention, and customer feedback and applications analysis. On the other hand, Banks lose and gain data depending on the legacy of their big data architecture (Skyrius et al., 2018, pp. 460). Banks have to carry out updates, trials, and maintenance of the big data analytics to prevent data loss while still gaining new data insights. If a bank loses data, they start losing customers’ trust, which is the prediction of a financial fall. Data loss is mainly made possible by failing to cope with real-time data issues such as modern tools and real-time data infrastructure.
Banking firms must target micro customer tenets to maximize big data analytics. They use this technique in various combinations such as customer demographics, past buying behavior, and media purchase behavior while incorporating the CRM data. They use reviews, customer accounts, marked locations, and social media activities to ascertain methods of maximizing big data. Banks use risk reporting and data aggregation principles to maximize data value. These principles allow banks to manage risks and executive effective strategic and quality planning. In this context, these principles are aligned to five basic themes: Personalization, Control, Portability, Security, and Advanced Analytics (Abdel-Fattah, Helmy & Hassan, 2019, pp 51). These are the overview areas in which big data is applied in the banking sector. In control, customer data is only usable with customer agreements and policies. Banks are held responsible for data security accountability. In personalization, banks have successfully created customer profiles to offer service differentiation. Advanced analytics assist banks in validation, testing, and explaining the use of modern models to customers. Portability makes it easier for customers to have full autonomy of their data.
The primary impact of big data analytics in the banking sector is giving these firms increased marketing muscle. These analytics have been used in functional aspects such as NPA monitoring, value, risk assessment, compliance, and fraud to necessitate overall success and improve timely decision-making. Banks have for long been data-driven sects, but digital banking has vastly included new dawn of operational thinking. One area which has greatly been impacted by big data is the customer insight level. Customers are currently interacting with their financial firms using digital platforms through analytical leveraging. In reverse, banks dissect information they garner from these platforms to enhance customer services, products, and other experiences (Skyrius et al., 2018, pp. 460). For example, banks use investment patterns, customer insights, needs, pain points, and other motivational trends to stay ahead of the competition by enhancing their experience. Another practical application area is the prevention and detection of fraud. This area has positively impacted the banking sector’s credit card, depositing, checking, and loan disbursement processes. Operational processes such as risk management have technically adopted big data analytics to cope with advanced analytics.
Using big data analytics to cut operational costs has some roadblocks which could be addressed. Amongst these challenges is the struggle to keep up with outdated systems. According to commentators, unlike other sectors, the banking sector is not overtly fast in innovation. This is evident since most world-famous banks rely on IBM mainframes and the use of big data under these legacy systems fails to cope with the increased workload. The entire system is at risk of failure once day-to-day volumes of data are stored. Another challenge is data maturity since banks use this extension in big data operations (Amalina et al., 2019, pp. 3633). Therefore, banks have to be more esteemed and sophisticated to cope with modern data analysis techniques to beat the maturity issue. In this context, data is mostly unstructured since the digital transformation has embraced various technologies in the banking sector (Zhang et al., 2020, pp.7). These legacies have overcome barriers and created opportunities that have only been achieved by analyzing unstructured data.
The format used in most banks, especially in the UK, is the MultiCash format which is standard for all banks. It has two files run through a spreadsheet program since it allows the intersection of data from multiple banks at a time. The model uses a BSC (Banking Communication Standard)-a system offering a common platform for different banks’ activities. The main challenge of this format comes with the harmonization standard, which may be threatened by the complexity and diversity of other incoming applications (Prokhorov & Kolesnik, 2018, pp. 415). The challenge might harbor the primary organization’s legacy standards. One negative effect is the signature processes which might become incompatible after handling diverse dialects.
Most banks incorporate Microsoft Power BI as the primary data visualization tool in the banking sector. The tool is crucial in collecting, processing, and analyzing data presentation in real-time. The only challenge is the installation since it requires high-level expertise. Banks use dashboards to monitor their financial performance and predict stock trends and customer trends through big data analytics (Ho et al., 2019, pp.57). Tableau is used in special cases to monitor, identify, and mitigate critical data insights risks. SAP Analytics cloud is used through the banking system ERP to make application processes successful. The data that my choice bank within the UK might be used by many is classification or categorical prediction. The method is vital in retrieving crucial information from single data and metadata (Lashari et al., 2018). It is reliable since data are grouped, such as in the Outlook email.
Machine Learning (ML) is the best fit for my business organization since it goes an upper hand in the competition after the dawn of big data analytics. It can execute performance indicators that stand core in sustainability and algorithmic efficiency. ML makes it possible to monitor software and hardware used in the digital banking economy, thus reducing time and cost in processes (Doerr, Gambacorta & Garralda, 2021, pp. 930). Such advantages make the firm reliable and attractive to customers. Machine Language techniques also accommodate day-to-day activities that use huge data volumes and effectively harness the necessary process to accomplish tasks.
Part 2
Introduction
Most banking firms incorporate big data analytics and machine learning in adopting effective inroads in the day-to-day activities’ toolkit. Big data analytics is mostly combined with machine learning to offer current opportunities to comprehend financial performances, systems, and the customer-based economy. Yet, these innovations’ advent has possible usability and practicality challenges. Technology has been adopted in both financial and insurance companies in the recent century. In the banking sector, technology mainly tackles customer loyalty and security issues. For instance, 50 years ago, typical customers walked miles to access banking activivite3s, unlike today, where they transact and contact banks through digital platforms. Big data, according to scholars, is the unstructured and structured information modeled in various formats but od similar contexts processed and managed using analytics to produce standard outcomes (Zhang et al., 2020, pp. 8). The data is prioritized according to value, volume, velocity, veracity, and variety to offer financial services and trends in banking sectors. Banks’ strategic and operation data applications incorporate complex vision, culture, skills, technology, planning, and governance relations. Challenges and opportunities of big data analytics lie in awareness, abundance, education, and analysis of the data analytics. The increased use of AI and innovative algorithms in the banking sector also increases the potential for harm. The work of Abdel-Fattah, Helmy & Hassan, (2019) will be discussed in depth in this paper. To ensure responsiveness, this self-enforced use of big data in banking is guided by professional and ethical principles such as fairness, accountability, integrity, and management. In this context, the paper analyzes application, challenges and opportunities, strategic and operational usability, and professional codes and ethics in using big data analytics in the banking sector.
Application of Big Data
Big data in banking and other financial sectors is used to overcome threats such as heavy workloads, security concerns, declining revenues, customer-related complaints, and other risks in the banking sector. Over the past decade, big data analytics have coordinated the banking buzzword, with most financial entities delving into the data science economy. This gave banks no exception and adopted data analytics following the advanced technology. According to scholars, a shift in technology will change people’s perceptions, structural behavior, and expectations. The advent of data technology has assisted banks in optimizing daily processes and streamlining overall operations, gaining a competitive edge through efficiency (Abdel-Fattah, Helmy & Hassan, 2019). Many banking institutions in the United Kingdom are sectioning to big data analytics to predict emerging trends and engineer their processes.
Data analytics in the banking industry are primarily used in supply, risk, and demand management. The traditional approach was mainly incorporated in the generation of dashboards and reports; today’s financial institutions engage big data analytics in meaningful ways. Banks are using technology to improve timeliness, usability, security, and customer-related aspects (Mikalef et al., 2019, pp. 272). For instance, big data analytics have mostly been used to create a personalized experience for customers. This applicability has been said to tailor effective customer experiences since it combines customer history and experience. The culture of the banking sector is shifting based on customer profiles, an online version permitting deposit and transfer of cash through digital platforms. Banks use these digital bargains to trace customer behavior and offer solutions targeted to their pain points. Therefore, profiling has made it easy for banks to retain and satisfy their customers. They use big data analytics to add to the generic view of their customers through actual online backup based on their behavior, preferences, and purchasing trends (Mikalef et al., 2019, pp. 270). Additionally, chatbots that have been made available by AI technology serve as assistive agents and offer timely responses to customers. The AI innovation has been used to analyze customer behavior and profile information to offer personalized responses. They have simplified customer services and online banking. This is why people have been able to make deposits and withdrawals using their mobile phones.
Big data analytics have also been employed in fraud detection and prevention. Based on current fraud statistics, identity fraud is a nuanced form of fraud according to 2017 records which suggest over 16 million cases. In this context, banking and financial firms have become keener to getting rid of this dilemma. Therefore, big data analytics has been diverted into banking systems to safeguard customer account information. The advent of big data came into existence with the increased use of business intelligence tools to evaluate risk and harbor fraudulent activities. Bid data information from these tools record and retrieve credit scores and individual interest rates and traces fraudulent acts. Also, these big data tools assist in market trend analysis and inform industry-wide and individual financial choices like inclined debt monitoring rates. Also, predictive analytics are used to gather information concerning debt-service ratios and cross-border debts used in predicting the potential financial crisis. The evolution of using business intelligence tools marks the end of traditional data and the dawn of valuable digital information. The complexity and advancement of technology have demanded vast and fast decisions to increase the importance of data-driven policies (Mikalef et al., 2019, pp. 270). Therefore, banks are relying on big data analysis...