BIG DATA ANALYSIS IN BANKING SECTOR

BIG DATA AND BANKING SECTOR

  Nonlinear relationships and regressions can be extracted from different data sets by using nonlinear system models rather than descriptive statistics in business intelligence in big data technologies. The banking sector, which is directly related to customer satisfaction, is also known as one of the most benefiting from this concept. In this sector, where data diversity and size are high, access to information and management can be facilitated by correct analysis.

 Due to the limited information access of the marketing and product development departments in banks, customer needs could be analyzed by traditional methods such as tracking account movements and segmentation. However, thanks to big data technologies, banks can now create a lifestyle score model from a mobile phone application, regardless of whether it is a customer or not, by monitoring the daily activities of individuals. By developing a new product or service with this data, they analyze how they can turn non-customers into potential customers while increasing their existing customer satisfaction. Customer dissatisfaction as well as customer satisfaction can now be analyzed faster thanks to big data. Complaints and comments written on banks via complaint sites can be processed on a word basis and separated. Although banks cannot produce a real-time solution for their customers' complaints, demands and suggestions, this will be possible very soon with the developing big data technologies. Thus, the bank will be able to offer a tailored solution proposal to the individual's need as soon as the customer enters the complaint. Another issue that requires deep analysis in the banking sector, where fraud attempts are inevitable due to intense money movements, is fraud detection.In the detection of fraud, following the information such as from which location and channel the customers do, and removing activity patterns are the most common methods. However, when it comes to information security, it is not sufficient to only monitor the key movements of the customers. Thanks to big data technologies, it is possible to prevent fraud attempts in real time by analyzing high volumes of data coming from different databases and data providers of all the examples that are experienced in the sector in this regard.

 Today, Big Data has reduced the analysis time in banks and enabled faster actions by offering the opportunity to integrate all data owned and not owned. This brings along operational opportunities in banks as well as the ability to meet increasing customer expectations and further advance their position in the industry. In this age when information sharing is intense and fast, it is no longer a necessity, but a necessity, for banks to closely follow the developments in the big data field and perform their analysis with methods appropriate for their own strategy.