Do we ever think of the amount of data we create each day? Every message we send, every credit card transaction, even every web page we open. This offers endless opportunities to leverage this data for the most forward-thinking businesses in many areas, and the banking industry is no exception. While digital banking is used by almost half of the world’s adult population, financial institutions have enough data at their disposal to rethink the way they work, becoming more efficient, more customer-focused, and ultimately more profitable.
The global financial services industry generates large amounts of structured and unstructured data every day by processing hundreds of billions of financial transactions and engaging interactions such as email, audio and video communications, call logs, blogs and social media mentions. One of the main drivers of this data boom is the increase in global payment volumes triggered by e-commerce and mobile payments. E-commerce also continues to grow dramatically, especially at a time when consumers are warned to shop in person as little as possible. The use of ATMs, the paperless processing and settlement of mortgages, peer-to-peer payments through applications as well as other mobile and remote digital banking services are becoming increasingly popular.
The use cases of big data in banking are the same when they first realized that banks could use huge data warehouses to generate actionable insights: to detect fraud, to simplify and optimize transaction processing, to improve customer insight, to optimize business execution, and ultimately to crowd to compete in the market. However, as you collect more data, the resulting insights and customer experiences become more accurate and meaningful. Say, it may offer a multi-channel approach that adapts and personalizes the customer experience by processing more than 29 transactions per second and integrating all this data into a single platform for statistical modeling and predictive analysis, without a physical location and it may offer full credit cards and other banking services, using big data analytics to evaluate and analyze nearly react real stays ahead of the online offerings of its old and more established competitors -time loan applications. a consumer-friendly feature that increases conversion rates tenfold for certain up-sell campaigns.
Dealing with new big data challenges: While the use cases for big data in banking have remained the same, the challenges have changed as data engineering technology has evolved. Speed is very important. The transition from the traditional data warehouse with ready-made hardware with a largely parallel engine to Hadoop has allowed banks to reduce their time spent gaining insight from their data from three months to a day or less. The introduction of cloud-based data processing has shortened this time frame even more. However, banks still tend to process data in monthly batches, which means they won’t see a trend for 30 days or longer. Apache Spark is a possible solution to this problem. Like Hadoop, it’s an open source big data analysis engine, but it’s faster, more scalable, and easier to use. It can also be used locally in cloud environments to capture and analyze streaming data in real time and get faster and more accurate answers to business questions.
Needs to be Managed and Governed of Big Data: Hadoop and Spark can move large amounts and diverse data to a data lake so that it can be moved to an on-premises or cloud data warehouse accessible to business users. However, it cannot guarantee that the data is suitable for use. Neither Hadoop nor Spark do data management or data management natively, so they cannot help business users understand what they own, what it means, or how they are used. They also don’t provide data origins, so users can see all the transformations their data takes on the path from source systems across the organization to analytics tools.
Modular and Commoditized: Banks addressed the costs of big data analytics, skill gaps, and infrastructure management issues by moving data processing from on-premises hardware to the cloud or hosted colocation facilities. However, when a local credit union and a multinational bank have the same access to AWS, Microsoft Azure, or a managed service provider, the ability to handle large amounts of data is no longer a competitive advantage. Banks need to be able to turn their data into smarter insights faster, then turn those insights into action to improve customer service, connect customers with information and products when and where they need it most, and with sensitive data, and protect customer accounts from threats. Today, analytics is becoming a important game-changer in the banking sectors. The financial services, banking, and insurance sectors are putting their full potential to develop the services that they supply their customers and expand their business opportunities.
Banking Sector need Big Data: Big Data can help the Finance industry not only organize its data, but also improve the customer experience. These are the reasons why the Finance and Banking Sector needs Big Data: 1. Employee Engagement 2. Operation Optimization 3. Customer Experience. The benefits of big data in banking are as: 1. Big data gives you a complete perspective on your business. This means you can make knowledgeable, data-driven resolution and, later, get business results. 2. It allows you to optimize and modernize your internal processes with the help of machine learning and artificial intelligence. As a result, you get a significant performance increase and lower operating costs. 3. In banking, big data analytics can be used to increase your cyber security and decrease risks. You can detect fraud and prevent potentially malicious acts using smart algorithms. Banks now have access to big data for use cases, such as creating new revenue streams through data-driven offerings, giving individual recommendations to customers, creating more productivity to increase competitive advantage, and providing better security and better customer service. Financial companies have to handle big data correctly to get immediate results. Banks have to be able to implement Big Data solutions to develop analytics platforms to predict customer payment behavior. A bank which has an idea of its customers’ behavior can shorten payment delays and earn more money while increasing customer satisfaction.
World and Big data: Big data is an essential issue in current business world. Everyday world creates 2.7 Zetabytes of data of data; 90% of the data in the world has been created in the last two years alone while in 2008, Google was processing 20,000 terabytes of data (20 petabytes) a day (Big data stats & facts, 2017). A large percentage of users perform analytics on large volumes of data using Hadoop which was not possible before. It was predicted to reach US$ 33.5 billion of annual revenue from the global big data market, with prediction suggesting this could double in size within the following four years (Statista, 2017).
Big Data and stage in Bangladesh: In Bangladesh, the concept of big data is still in primary stage. Banks and non-banks in Bangladesh need to take initiative toward data development procedures. The data are collected primarily from customer financial behavior such as deposit behavior, credit card transactions, loan statements and ATM transactions using various software. The banks used in the sample, replied that they are acquainted with big data and have plan to exploit the potentiality of big data analytics very soon. These data are stored by IT department of the banks, that is, data is created through online banking behavior of the customer and use in order to the need. Some software is used for various purposes and to collect data for the customer. These data are mainly designed to collect for three sources; first, Core Banking Software is used for opening a bank account by deposit and loan customers, and recording their transactions, second, Switching Software is required to manage ATM and POS network, third, to collect data related to credit card issuance and transaction authorization, Credit Card Software is used, and fourth, for mobile financial services through banking network, a Mobile Banking Software is used.
Potentiality of Big Data in Bangladesh: Bangladesh has large potentiality of using big data to increase efficiency of banks since unbanked population is decreasing sharply and the use ICT is increasing gradually. BTRC reports that currently the mobile subscribers exceeded to 150 million, that is, more than 90 percent population use mobile phones in Bangladesh. Internet user on the other hand, surpasses to 85 million, that is, more than 50% of the population use either mobile internet or other sort of internet (BTRC, 2018). The good penetration of ICT refers to the strong innovation of cloud base data base so that the stakeholders can explore in order to the need. This clearly suggests opportunity for the banks to exploit big data analysis so that they can collect customer data through online financial activities. When customer use more online financial services, it helps bank to store more data into their database to increase banking activities considering some major functions, such as: Customer transaction analysis, Customer retention analysis, Customer risk management, Security and fraud analysis. In Bangladesh, finance industry plays essential role in national economy. Proper information can help potential stakeholders to maximize their goal too. Traditional or unstructured data is difficult for people who do not have minimum knowledge to analyze data for their need. Thus, if this unstructured or traditional data are collected through structured way, it will offer notable advantage for banks in Bangladesh.
Required framework for Big Data analysis- in the banks: Big Data Analysis assimilation is considered a capability that provides a competitive advantage for organizations in terms of operating performance and supply chain performance. Big Data Analysis adoption functions as a form of knowledge capability and an intangible resource for firms to enhance their performance. In terms of creating business value from big data analysis acknowledge that a big data analysis solution brings higher business value and higher competitive advantage.
Big Data and Technological readiness: Technological competence, or organizational readiness is the state to be prepared, both in terms of facilities and skills, to ensure that firms qualify when using new technologies, in addition, IT infrastructure provides the technical basis for the smooth implementation of Big Data initiatives. Therefore, small and medium-sized enterprises need adequate technical resources, and enterprises cannot implement without adequate technical resources. Data characteristics imply the size of data in terms of volume, velocity, and variety. When banks face the characteristics of Big Data, they tend to use Big Data analysis to optimize what data brings. In the modern world, the internet and mobile phones allow banks to interact more frequently with customers and to collect more data. These data increase volume, velocity, and variety in structural/semi-structural or unstructured formats. In addition, the development of technology and collaboration with third parties will enable banks to retrieve more data from diverse sources with different data types. However, banks should more concern with data quality and Big Data integration into usage. Data quality refers to the consistency and integrity of the collected data. This means that banks with higher data quality will increase their use of Big Data analysis.
Implementing and maintaining complex Big Data analysis requires staff knowledge and skills i.e, techniques combine data analysis, business knowledge, and IT skills. BDA personnel should be capable of dealing with emerging technologies such as natural language processing, text mining, video/voice/image analytics, and visual analytics. It is suggested that the relevant Big Data management and analytic competency can be achieved through training and external experts. Employees or data scientists should use high-level data science practices to understand the business domain in order to comply with requirements and provide actionable business outcomes. Therefore, technological innovations will influence the risk management sector in banking operations. In particular, the wave of applications for big data analysis technologies will benefit risk managers at banks to make smarter decisions at lower costs. For instance, big data analyzes customers’ information to help banks make accurate decisions about the provision of services such as retail lending and financial crime detection. The beliefs and participation of top-level management have a significant impact on how organizations embrace IT transformations. It is argued that it is difficult for banks to achieve higher levels of innovation capability without the support of management. Another argument is that the strategic importance of top-level management support for big data use.
Concluding thoughts: It is consistent with the three capabilities in dynamic capability: adaptive capability, absorptive capability, and innovative capability. Therefore, it suggests that banks should carefully consider these capabilities when planning to use big data. This means that the practical use of big data should be directed towards creating adaptive, absorptive and innovative capability. According to our conceptual framework, the use of big data to create these dynamic capabilities will affect bank performance.
