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 bank...