Impact of Big Data Analytics on Banking Sector: Learning for Indian Banks

December 15, 2022

As CEO at Eastern Peak, a professional software consulting and development company, Alexey ensures top quality and cost-effective services to clients from all over the world. Alexey is also a founder and technology evangelist at several technology companies. Previously, as a CEO of the Gett (GetTaxi) technology company, Alexey was in charge of developing the revolutionary Gett service from ground up and deploying the operation across the globe from New York to London and Tel Aviv.

Thinking about the influence of big data on the financial sector and its services, the process can be highlighted as a modern upgrade to financial access. In particular, online transactions, banking applications, and internet banking produce millions of pieces of data in a single day. Because managing these internet financing services has major impacts on financial markets [57]. Here, Zhang et al. [85] and Xie et al. [79] focus on data volume, service variety, information protection, and predictive correctness to show the relationship between information technologies and e-commerce and finance. Big data improves the efficiency of risk-based pricing and risk management while significantly alleviating information asymmetry problems. Also, it helps to verify and collect the data, predict credit risk status, and detect fraud [24, 25, 56].

Big Data in Banking and Finance

This paper focuses on the implications of the expanding footprint of big techs in finance. Financial institutions are not digital natives and have had to go through a lengthy conversion process that necessitated behavioral and technological changes. The Big Data banking industry has experienced considerable technological advancements in recent years, allowing for convenient, tailored, and secure solutions for the business.

Big Data in Banking and Finance

Decentralized approaches are effective when there is a need to stay close to the business units to infuse domain expertise and drive adoption (and when a lower priority is assigned to scaling quickly and consistently across business units). Centralization of the analytics organization is typically better suited to a company that is starting its analytics journey and seeking to establish groupwide capabilities and consistent policies and language. A hybrid state with a COE defines the direction of the strategy, stays abreast of the latest methodological advancements, provides shared analytics services, and moves the organization towards an agile culture. Our team has vast experience implementing fintech products of different complexities as well as building big data solutions from scratch. Among other projects, we helped Western Union implement an advanced data mining solution to collect, normalize, visualize, and analyze various financial data on a daily basis.

Learn how these components can propel your insurance business into the future. Transparency in data usage policies is essential to maintain customer trust, but achieving this transparency can be very challenging. Our experts will consult you on seamlessly transforming your data, considering all opportunities as price, operating costs, efficiency, loyalty, and many more. Prior to embarking on a trip to Barcelona, Avery notifies their bank that they’ll be traveling out of the country so that it won’t put a freeze on their account while they’re abroad. First, raw computing power create hardware that is capable of storing the huge amount of data that is generated around the clock in the digital age.

  • Forty percent of banks follow a hybrid approach that concentrates analytics talent in COEs, providing solutions to the entire bank and balancing analytics efforts within business units.
  • Without this emphasis, business teams will fail to understand the power of analytics, and in turn, relationship managers, marketing teams, and credit underwriters will not be motivated to make the necessary changes in mind-set.
  • Unstructured data is enormous and poses a variety of challenges processing to derive a value from it.

With so many different types of data and their combined volume, it’s no surprise that businesses struggle to keep up. This becomes even clearer when attempting to separate the useful data from the useless. In addition, in the event of financial terrorism, they can actively collaborate and share insights gained from their tools of Business Intelligence and Big Data Analytics with governmental agencies to mitigate such risks. Retail banks, investment banks, NBFCs, private equity firms, and others all have a dedicated Risk Management department that heavily relies on Big Data and Business Intelligence tools. Customer segmentation allows banks to better target their clients with the most appropriate marketing campaigns.

Big data, machine learning, AI, and the cloud computing are fueling the finance industry toward digitalization. Large companies are embracing these technologies to implement digital transformation, bolster profit and loss, and meet consumer demand. While most companies are storing new and valuable data, the question is the implication and influence of these stored data in finance industry. In this prospect, every financial service big data in trading is technologically innovative and treats data as blood circulation. These services are influencing by increasing revenue and customer satisfaction, speeding up manual processes, improving path to purchase, streamlined workflow and reliable system processing, analyze financial performance, and control growth. Despite these revolutionary service transmissions, several critical issues of big data exist in the finance world.

Predictive analytics-based decisions consider everything from the economy to client segmentation to corporate capital to identify potential hazards such as faulty investments or payments. Modern banking apps provide easy access to data-driven technology at customer’s fingertips. For e.g., certain apps can make accurate predictions as to whether customers are likely to exceed their credit limits and alert them on accidental overcharges on their account. Big Data integration has quickly become an indispensable part of the new generation of smart, self-teaching appliances resulting in a metamorphosis across multiple facets of society and also people’s daily lives. Devices generate as well as consume staggering amounts of data, and leveraging this data, learning, and recognizing patterns and implementing changes / processes based on deep analysis of such data has empowered businesses like never before. Financial institutions that apply this technology better understand customer needs and make accurate decisions.

The banking industry is a prime example of how technology has revolutionized the customer experience. Gone are the days when customers had to stand in line on a Saturday morning just to deposit their paycheck. Customers can now use their mobile phone to check their account balances, deposit checks, pay bills, and transfer money — there’s no need for them to even leave the house.

To spark your creativity, here are some examples of big data applications in banking. While the share of potentially useful data is growing, there is still too much irrelevant data to sort out. This means that businesses need to prepare themselves and bolster their methods for analyzing even more data, and, if possible, find a new application for the data that has been considered irrelevant. With so many different kinds of data and its total volume, it’s no surprise that businesses struggle to cope with it.

Every financial company receives billions of pieces of data every day but they do not use all of them in one moment. The data helps firms analyze their risk, which is considered the most influential factor affecting their profit maximization. Cerchiello and Giudici [11] specified systemic risk modelling as one of the most important areas of financial risk management. It mainly, emphasizes the estimation of the interrelationships between financial institutions.

Financial institutions are putting Big Data to work in big ways, from boosting cybersecurity to cultivating customer loyalty through innovative and personalised offerings. Identifying and tackling one business challenge at a time and expanding from one solution to another makes the application of big data technology cohesive and realistic. More importantly, the finance sector needs to adopt a platform that specializes in security. Tracking data at a granular level and ensuring that valuable information is accessible to key players will make or break a data strategy.

At almost two-thirds of banks applying analytics, C-suite sponsors evangelize their programs and give progress reports on strategies to the broader organization. These communications should emphasize how analytics can be a complement—or counterpoint—to established practices. So, if you want to discuss opportunities and big data implementation options in banking,  request for a personal consultation using our contact form.


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