Big Data Analytics has become one of the main drivers of innovation in the banking industry. According to the IDC Semiannual Big Data and Analytics Spending Guide of 2016, investments in Big Data Analytics in the banking industry totaled $20.8 billion. The intense amount of investment makes the banking domain one of the dominant consumers of Big Data architects, solutions and bespoke tools.
The PwC Global Fintech Report 2016 shows the allocation of funds within the amount of investment mostly targets customer support, risk assessment, decision making support and researching for new profit opportunities. Also, the investment in new markets, lessening the time-to-market and funding the blockchain projects. The trend has only increased the numbers in 2018. According to the GDC prognosis, the amount of data generated every second will grow up to 700% by 2020. The financial and banking data will be one of the cornerstones of the Big Data flood, and being able to process it all would be competitive in the financial institutions and banks. The Big Data flows can be really described in 3 v’s in businesses: variety, velocity, and volume.
To be precise, Big Data analysis real time can be used to make business decisions accordingly. It can be applied to the following activities:
The companies must evolve and grasp new technologies if they want to succeed. Adopting Big Data analytics and diffusing it into the existing banking sector workflows is one of the key elements of surviving and prevailing in the rapidly evolving business environment of the digital millennium. In the last 10 years, the banks invested heavily into modernizing their offers and providing mobile access to their services. In the next 5 years, they will have to learn to empower their operations with Big Data analytics, AI/ML algorithms, and other high-tech tools.
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