The data revolution over the past decade has found a special place in the Indian digital banking space, given the vast amount of customer data they have been storing for decades. This crucial data has now unlocked the secrets of customer experience, money movement and helped prevent disasters and major fraud.

Banks in India are getting the most out of data analytics as they can now quickly and easily extract highly accurate insights from their data and convert it into business insights to direct business to their customers. Big Data Analytics in Global Banking Services Market is expected to register a CAGR of 22.97% during the period 2020-2026.

The main factors contributing to Big Data Analytics in the banking sector are the significant growth in the amount of data generated and government regulations.

As technology advances, the number of devices consumers use to initiate transactions also proliferates (such as UPI), increasing the number of transactions thus allowing banks to use customer data for commercial purposes. ‘to analyse. This offers information that enhances the banking experience.

A cloud-enabled Big Data Analytics solution enables banks to store all their data in an elastic and cost-effective environment while providing the processing, persistence, and analytics capabilities needed to acquire business insights that drive business, improve customer experience and manage risk. .

A Data analysis The platform stores and organizes structured and unstructured data and means to organize massive amounts of wildly 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 to the cloud, an increase in digital disruption, and technological advancements such as the integration of natural language processing, machine learning, and networking. of neurons.

Data Analytics for the banking sector can be described in 3 Vs: Variety, Velocity and Volume.

Variety represents the fullness of data collected by banks, processed and stored. From transaction details and history to credit scores and risk assessment reports, banks have treasure troves of this data.

Speed means the rate at which new data is added to the central banking system which is frequently updated and used for real-time analysis.

Volume means the amount of space the data will take to be stored. Traditionally, banks collect and store huge volumes of financial data about their customers. However, the 3 Vs above are useless if they don’t lead to the 4th – Value. For Indian banks with a large customer base, this means they can integrate Data Analytics results in real time and enable faster business decisions while reducing costs.

Data analytics has benefited the banking industry in the key areas below.

Better target customers and ensure growth: By better understanding customers and using analysis of their transactions and activities, banks validate their services with their customers’ needs, resulting in higher levels of retention and acquisition.

Improved risk assessment: As banks will be able to assess the risk profiles of their credit applicants in much more detail, they will also be able to improve their credit ratings. Data analysis has also advanced early warning and data collection systems. All of these features have helped banks reduce risk costs and become aware of fraud sooner.

Improve productivity and decision making: With the benefit of advanced analytics, banks provide faster and more accurate responses to regulatory requests.

Data analysis helps to make better decisions for daily activities: By underestimating optimization techniques using data, banks have optimized the location of branches, ATMs and ATMs, and even cash requirements at the branch and ATM.

More business opportunities: By collecting data from customers, data analytics have enabled banks to develop new business models and revenue streams in the form of liability insurance, mutual funds, and more.

Risk management: Banks have worked to put in place a comprehensive risk management solution, as these risks have an impact on their income. Data Analytics has helped banks identify risks in near real time and employ appropriate business strategies to mitigate risk.

Read also | Data analytics and AI to create vast opportunities in loan addressing: Haryyaksha Ghosh, CDO, Aadhar Housing Finance

The banking industry as a whole has been working towards this analytics transformation through culture, capabilities and technology, which is essential for the success of Data Analytics within the organization.

In order to mature in data analytics, banks build the right organizational culture and support it with the right skill sets and technology components. A structured approach taken by banks to adopt the analysis is described below.

A cultural shift from a “Data as an IT asset” culture to a “Data as a Key Asset for decision-making” culture:

Effective big data initiatives require cultural shifts within the organization and a concerted move towards data-driven behavior.

To carry out big data programs, banks have worked to secure full management sponsorship for analytics initiatives, develop and promote an enterprise-wide analytics strategy, and ‘integrate analytics into core business processes. Essentially, banks are gravitating towards a model where analytics is an enterprise-wide priority and an integral part of decision-making across the organization.

Develop analytical talent with a targeted recruitment process and ongoing in-house training programs:

As a first step towards building data analytics expertise, banks have implemented a well-defined recruitment process to attract highly sought-after analytics talent. Additionally, disparate analytics teams are brought together in an Analytics Center of Excellence (ACoE) that fosters the sharing of best practices and supports skills development. Banks invest in the continuous training of their analytical staff on new tools, techniques and technologies. Specialized training programs are being developed for line-of-business personnel to train them in the use of analytics to improve decision-making.

Establish a solid data management framework for structured and unstructured data:
The quality, accuracy and depth of data determine the value of business information. Therefore, banks are establishing robust master data management frameworks to formalize the collection, storage, and use of structured and unstructured data. Additionally, banks have adopted advanced analytics techniques such as predictive and prescriptive analytics that help predict future customer behavior. This in turn increased opportunities for cross-selling, price optimization and targeted offers.

Big Data analysis for the banking sector has benefited the industry in all sectors of activity. The potential for growth and evolution of analytical platforms and benefits offered to banks remains immense.

However, the banking sector must exercise caution. As various data analysis tools and techniques can reap huge benefits, one should never lose sight of the importance of data security. Banks should not hesitate to make significant investments in building a robust data governance model and data encryption tools. It may seem insignificant, but they are imperative to overall success.

The impact of big data analytics in banking has been revolutionary. It has not only transformed the banking landscape, but also the entire financial sector. The measurement of data analytics in banking is growing rapidly and has provided many opportunities for banks to improve their business and provide enhanced services at marginalized costs. The data analytics opportunity is quite simply an opportunity to redefine the rules of the game for banks.

Opinions expressed by: Bhargav B. R, Tech Lead (AL, ML & Analytics), Karnataka Bank.

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