The data revolution during the last decade has found a special place in the Indian digital banking space, considering the vast amount of customer data they have been storing for decades. This crucial data has now unlocked secrets of customer experience, money movements, and helped prevent major disasters and frauds.
Banks in India are reaping the most benefits from Data Analytics as they now can extract highly accurate information quickly and easily from their data and convert it into business insights to drive business to their customers. Big Data Analytics in the Global Banking market is expected to register a CAGR of 22.97 per cent during the period 2020-2026.
The major factors contributing to Big Data Analytics in the banking sector are the significant growth in the amount of data generated and governmental regulations.
As technology advances, the number of devices consumers use to initiate transactions is also proliferating (such as UPI), increasing the number of transactions thus allowing banks to utilise customer data for analytics. This offers insights that improve the banking experience.
A Big Data Analytics solution enabled on cloud allows banks to store all of its data in a cost-effective, elastic environment while also delivering the processing, persistence, and analytic capabilities required to acquire business insights that drive business, improve customer experience and manage risk.
A Data Analytics platform stores and curates structured and unstructured data and ways for organising massive amounts of extremely different data from multiple internal and external data sources. The rise of cloud deployment in the banking industry is driven by a shift in preference toward the cloud, an increase in digital disruptions, and technological advances such as the integration of Natural Language Processing, Machine Learning and Neural Networks.
The Data Analytics for the banking industry can be described with 3 V’s : Variety, Velocity and Volume.
Variety stands for the plenitude of data collected by the banks, processed and stored. From transaction details and history to credit scores and risk assessment reports — the banks have troves of such data.
Velocity means the speed at which new data is added to the core banking system which is frequently updated and used for real time analysis.
Volume means the amount of space data will take to store. Banks traditionally havebeen collecting and storing huge volumes of customer financial data. However, the above 3 V’s are useless if they do not lead to the 4th one – Value. For Indian banks with a huge customer base, this means they can integrate the results of Data Analytics in real-time and enable faster business decisions while driving down costs.
Data Analytics has benefitted the banking industry in the key areas below.
Better customer targeting and ensuring growth: By understanding clients more fully, and by using analytics of their transactions and activities, banks are validating their services with their customers’ needs, resulting in higher levels of retention and acquisition.
Enhancing risk assessment: As banks will be able to assess the risk profiles of their credit applicants in much greater detail, they will also be able to improve their credit assessments. Data analytics has advanced early-warning systems and data collection as well. All of these features have helped banks to lower their risk costs, and to become aware of fraud more quickly.
Improving productivity and decision- making: With the advantage of advanced analytics, banks are providing faster and more accurate responses to regulatory requests.
Data Analytics enable better decisions for everyday activities: By understating optimisation techniques using data, banks have optimised Branch, ATMs and counters location, and even cash requirement at the Branch and ATM.
More business opportunities: By collecting data from customers, data analytics have enabled banks to develop new business models and new sources of income in the form on third party insurance, mutual funds etc.
Risk management: Banks have been working towards achieving a comprehensive Risk Management solution as these risks impact their revenue. Data Analytics has helped banks identify risks in near real time and use appropriate business strategies to mitigate the risks.
The banking industry as a whole has been working towards this analytical transformation across culture, capabilities and technology, which is critical for the success of Data Analytics within the organisation.
In order to mature in data analytics, banks are building the right organisational culture and backing it up with the right skill sets and technological components. A structured approach taken by Banks to adopt analytics is described below.
A cultural shift from a ‘Data as an IT asset’ to a ‘Data as a Key Asset for decision-making’ culture:
Effective big data initiatives require cultural changes within the organisation and a concerted shift towards a data driven behaviour.
To drive successful big data programs, banks have been striving towards full executive sponsorship for analytics initiatives, developing and promoting a company-wide analytics strategy, and embedding analytics into core business processes. In essence, banks are gravitating towards a model where analytics is a company-wide priority and an integral element of decision-making across the organisation.
Develop analytics talent with a targeted recruitment process and continuous in – house training programs:
As a first step towards building expertise in data analytics, banks have established a well-defined recruitment process to attract the highly prized analytics talent. Further, disparate analytics teams are consolidated into an Analytics Centre of EXcellence (ACoE) that promotes the sharing of best practices and supports skills development. Banks are investing in continually training their analytics staff on new tools, techniques and technology. Specialised training programs are being developed for line of business personnel, to train them in the use of analytics to enhance decision-making.
Establish a strong data management framework for structured as well as unstructured data:
The quality, accuracy, and depth of data determine the value of business insights. Consequently, banks are establishing robust master data management frameworks to formalise the collection, storage and use of structured as well as unstructured data. Additionally, banks have adopted advanced analytics techniques such as predictive and prescriptive analytics that enable Prediction of future customer behaviour. This in turn has increased cross-selling opportunities, pricing optimisation and targeted offerings.
Big Data Analytics for banking has been benefitted the industry across business verticals. There is still immense potential for growth and evolution of the analytical platforms, and the advantages afforded to banks.
However, the banking industry should exercise caution. As various Data Analytics tools and techniques can reap huge benefits, one should never lose sight of the importance of data security. Banks should not shy away from making significant investments in building a vigorous data governance model and data encryption tools. This might seem insignificant but they are imperative for overall success.
The impact of big data analytics in the banking sector has been revolutionary. It has not just transformed the landscape of banking but also the entire financial industry. The measure of data analytics in the banking sector is quickly expanding and has provided numerous opportunities for banks to improve their business and deliver improved services at marginalised costs. Data analytics opportunity quite simply is an opportunity to redefine the playing field for banks.
Views expressed by: Bhargav B. R, Tech Lead (AL, ML & Analytics), Karnataka Bank.
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