The digital transformation of banking industry is not without the essentiality of offering financial services to the rapidly growing digital customers enjoying such services as provided. Though it is known that financial services are generally conservative and a traditionalist in the business segment of the world as a waiting phase before considering and adapting a new technology is a common trait. However, a change in notion is present in the regards of embracing digital transformation by the banking industry in this modern society [ Burns, M. (2019) ].

Burns [ Burns, M. (2019) ] revealed the causative factors for this embrace as highlighted below:

-i- Scaling up, to process rapidly growing volume of new transactions and also to avoid downtime of, the company’s infrastructure while servicing digital customers;

-ii- Growing importance of cryptocurrencies as it is now regarded as a legitimate, widely accepted, highly convenient, and general-purpose usable tools for carrying out financial transaction by all and sundry;

-iii- Biometrics, namely facial, fingerprint and voice identification of clients is now a vital part for the procedures for verifying financial transactions as used by corresponding institution as it increases security of transactions;

-iv- Automation, as described in my previous article [ Biancuzzi, D. (2019) ], is now a vital component for handling repetitive process as the case maybe in the banking sector, offering effectiveness and efficiency;

-v- Analytics, offering maximum understanding of organization’s customer behavior and discovery of underlying factors behind business performance in order to serve better decision making;

-vi- Artificial Intelligence, though still rudimentary, enables customer profiling, better customer management, credit scoring and financial advice based on historical data;

-vii- Data sharing and integration leading to better comprehension and viable prediction of individual customer’s requirements and needs as well as offering a real-time data repository facility for multiple access by different employees within an organization.

Thereby effecting a disruptive digital transformation in the banking sector as well. The utmost advantage of digital transformation in the banking industry saw to the investment of about US$ 9.7 billion by various banks globally in other to accelerate the digital transformation of the industry as reported by Deloitte [ V. Srinivas V. & Ross A. – Deloitte Insights (2019) ]. Reportedly, Deloitte [ V. Srinivas V. & Ross A. – Deloitte Insights (2019) ] stated “for many retail banks, online and mobile channels have become as important – if not more important – than branches and ATMs” as digital engagement as become the key to optimizing the consumer experience as organizations enjoy customer acquisition through engineered digital experience that connects to customers’ emotional connections while providing desired satisfaction.

As a result of this digital transformation, vast volume of data has the prowess to power the new era of digital banking – providing the right product at the right time and to the right person having thoroughly understand the customer in question. This vast volume of data is what is being referred to as Big Data. Big Data is the new oil for financial institutions and looking at many different data points provides meritorious advantages as explained by Zennon Kapron, Founder and Director of market research firm located in Kapronasia, and reported by HSBC [ HSBC (2018) ]. Another reported statement by HSBC [ HSBC (2018) ] revealed that Big Data has the massive potential of revolutionizing bank offers to her customers as they crave to obtain efficient end services through aggregated information shared among relevant institutions.

Embracing Big Data by the banking industry is not solely tied to satisfying customers and the digital transformation is not only necessary for customer acquisition. Other factors that drove banks to embrace digital transformation and harness Big Data include management of risks of fraud and errors as well as cutting costs. The need for banking system to protect against fraud and terrorist financing, avoiding heavy penalties incur due to non-compliance to money laundering regulations, customer identity validation and verification as required by banks also constitute the need for digital transformation and Big Data usage as reported by Sangeeta [ Sangeeta (2018) ]. Instance of non-compliance penalties include the Deutsche Bank ($204 million) in 2017, Standard Chartered ($327 million) and HSBC Group ($1.9 billion) in 2012 and also, Punjab National Bank was reported for being unable to detect non-performing assets (NPA) [ Sangeeta (2018) ].

The adoption of big data analytics in bank industry was valued and then reported to globally reach US$14.83 million by 2023 [ Sangeeta (2018) ] though the worldwide revenue for Big data and business analytics solutions is estimated to reach US$260 billion by 2022 [ Aleksandrova M. (2019) ] as it is being applied to fraud detection and management, Customer Analytics, Social Media Analytics, Operational Intelligence, and Customer Relationship Management. In this industry, Big data conceptualized data related to customer information and preferences with respect to banking products such as operational data, loans and investment services and also any other data exchanged between banks and financial partners. Broadly the banking Big Data can be categorized into customer, operational, financial, market and risk assessment data which exist as both structured and unstructured data from various sources including mobile apps, markets, credit scores, chatbot, customer behavior etc. [ Sangeeta (2018) ].

Sangeeta [ Sangeeta (2018) ] further reported the application of big data analytics in real time had added “value” to banking such as innovative banking products and services, operational efficiencies, profits and also risk assessment, compliance and reporting. Thus, key banking goals such as increased customer confidence, maximizing ROI on marketing investments, mitigating risks as well as expansion of banking operations are well and easily achieve through Big Data analytics which can be segmented in customer analytics that handles key to outperforming existing competitors across the full customer lifecycle, and also risk and operational analytics that assist banks to achieving operational efficiencies while reducing risks. Overall, Big Data in Banking helps in Customer Relationship Management (Higher Customer Satisfaction (CX) and Customer Retention) (CRM), Marketing Management (customer engagement for longer Customer Lifecycle Value (CLV), customer segmentation for tailored marketing campaigns, new customer acquisition, and Marketing Mix), Operational Optimization, Fraud Management, Risk Management, Regulatory Compliance, and lastly Financial Management as discussed by Sangeeta [ Sangeeta (2018) ].

Big Data in banking are with some challenges as revealed by Aleksandrova [ Aleksandrova M. (2019) ]. First of the challenges revolved around the legacy systems being used by most banks which struggles to keep up with the new technologies and tools. 92 of 100 world leading banks uses IBM mainframe that can not handle the growing workload of trying to collect, store and analyze the required amounts of data without putting the entire system at risk. The second major challenge is focused on the risk facing the accumulated data. Aleksandrova [ Aleksandrova M. (2019) ] espoused further that 38% of all organizations in the world are ready to handle threat, thus, ensuring accumulate and processed data remain safe is of the major challenges faced by banks as the data are concerns entity wealth. The last major challenge to be highlighted is the growing nature of the Big Data as there exist continuous production of structured and unstructured data leading to the challenge of sort valuable data from the irrelevant ones.

Digital transformation of the banking industry and the emergence of heterogeneous Big Data in the same industry fuels the data driven innovation peculiar to this industry now and also in the future. Through Big data, personalization of customer experience, targeted promotional and marketing services, prioritize loan acquisition services, predictive analytics modelling using customers’ credit history, automated system which can be integrated with robotics, AI based solutions, dealing with regulatory compliance issues, and many other services can be offered by banking institutions to their respective customers [ Ovenden J. (2019) ].