A guide to enhancing Bank’s intelligence with data-driven BI & Banking Analytics

A guide to enhancing Bank’s intelligence with data-driven BI & Banking Analytics

A guide to leveraging BI and analytics in banking to derive actionable insights from data, enhancing customer service, risk management, and operational efficiency....
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Authored by
NSEIT Data and analytics practice

Why do we need Banking Analytics?

Banking customers generate an astronomical amount of data every day through hundreds of thousands of individual transactions and various touchpoints. To give you a number of how much information this can be, we generate 2.5 quintillion bytes of data every day! This data holds untapped potential for banks and other financial institutions that want to better understand their customer base, product performance, and market trends. As a progressive bank, you now have the opportunity to boost your sales and profitability and tap the untapped data by tapping into the customers’ anticipations using Business Intelligence (BI) and Data Analytics in banking. But before jumping onto Banking Analytics let’s figure out what are the types/forms of data that banks generate.

Types/Forms of Data –

The amount of data that has been generated by banking customers is humongous – but that data comes in all kinds of types and forms which also raises issues of data quality and management. Following are the three basic categorizations of data types –

Structured Data – This type of data is highly organized and exists in a fixed format, such as a CSV file.

Unstructured Data – This data has no clear format. An example could be emails since they are difficult to process. They can be found hidden within your networks, machines, etc.

Semi-structured Data – Data that is semi-structured might initially appear unstructured but contains keywords that can be used for processing.

Unstructured data and semi-structured data are also called dark data – as per “The Databerg Theory” as these categories of data require advanced processing techniques in order to be translated into valuable, actionable information therefore, banks and other financial institutions are building up data infrastructure that can help generate insights from these data  categories. Data analytics in banking also helps in fraud detection and prevention. By analyzing patterns and anomalies in transaction data, banks can proactively identify suspicious activities and mitigate risks in real-time.

How Analytics is redefining the way banks function

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 to check their balance or used to visit local bank branches for an FD/RD. Customers can now use their mobile phones to check their account balances, deposit checks, pay bills, transfer money, and make investments — there’s no need for them to even leave the house.

These self-service features are fantastic for customers, but they are one of the main reasons why traditional banks are struggling to compete with new gen-digital banks or NEO banks. Data analytics in banking allows traditional banks to gain deeper insights into customer behavior, preferences, and needs. Since customer activity now occurs mostly online, certain in-person services that brick-and-mortar banks have been known to provide are no longer relevant to customer needs.

This is where adopting data analytics strategies and tools becomes so important to the banking industry. Using both personal and transactional information, banks can establish a 360-degree view of their customers in order to:

  • Track customer spending patterns
  • Segment customers based on their profiles
  • Incorporate retention strategies
  • Collect, analyze, and respond to customer feedback
  • Implement risk management processes
  • Personalize product offerings

Benefits of Banking Analytics –

Following are some of the cited examples of using banking analytics –

Complete 360 views of the customers and their profiling: Customer segmentation has become commonplace in the financial service industry because it enables banks to separate their customers into neat categories by demographic, but basic segmentation lacks the granularity banks require to understand their customers’ needs. Therefore, banks need to implement banking analytics to take segmentation to the next level by building detailed customer profiles.

Understand personalization and opportunity for upselling and cross- selling: Businesses are 60%–70% more likely to gain profits by tapping and catering to existing customers than they are to prospects, which means personalization, cross-selling, and upselling present easy opportunities for banks to increase their profit share — opportunities made even easier by data analytics in banking. Moreover, business analytics in banking aids in risk management and fraud detection.

Analytics in Credit Risk and Collection: To reduce NPA and increase profitability, banks have to lend to the right type of customers. To identify such customers, you need advanced analytics to understand the types of customers, monitor collections, and conduct a ‘What If’ analysis around various scenarios to accurately predict non-payments and reduce delinquencies.

Analytics in Finance and Treasury: When it comes to determining the interest rates and forecasting NII, banks need to deploy analytical tools for an in-depth view of their overall fund’s situation and FTP. By deploying analytics powered by machine learning with advanced algorithms like random forests and gradient boosting, banks can accurately establish risk tolerance levels and develop fool-proof intelligence mechanisms.

Streamlined customer feedback and retention: With data analytics infrastructure in place banks can truly create immersive customer experiences but they also streamline their customer feedback. With in-depth customer profiles at your fingertips, it’s easier to build stronger, longer-lasting customer relationships that drive customer retention and also get feedback.

How India Inc. is warming up to the potential of Analytics

Indian firms had an analytics budget of $2 billion in 2021, with an average of $27 million per company, while the Private Sector Banks had the highest annual average analytics budget per company at $88.5 million, followed by E-commerce enterprises at $50 million.

The numbers indicate that Analytics has evolved beyond a mere set of discrete projects. It also conveys that the BFSI sector in India is keen to adopt analytical techniques that in the past might have been dismissed as the prerogative of top-tier IT enterprises. However, by putting their strategic and financial muscle into analytics banks are now moving ahead to make data and analytics work for them instead of the other way around, and even turning it into a true business discipline. Businesses that are able to quantify their gains from analyzing big data reported an average 8% increase in revenue and a 10% reduction in overall costs, according to a 2015 survey from BARC. This means the adoption of business analytics in banking has allowed banks to harness the power of data to drive revenue growth, reduce costs, enhance operational efficiency, and improve decision-making.

A real-world precedent of a leading bank in India

Here’s an ideal example of how BI powered by data and analytics enabled a leading bank in India to drive product profitability by a whopping 23%. The details of this distinguished project by NSEIT have been comprehensively captured in this interesting case study – Driving Product Profitability with Business Intelligence. Download it to learn about the excellent solution that streamlined the bank’s entire customer experience journey and propelled it to achieve some truly remarkable figures in terms of profitability, customer engagement, and operational costs.

How banks can leverage the true power of data & analytics

The matured BFSI sector in India is in a strong position when it comes to the massive customer data it has collected from historical transactions. Expert Tech companies like NSEIT can help banks to utilize BI and analytics to take a 360-degree view of their customer’s financial health and deliver the most personalized customer experience. Check out our Data Analytics offerings in banking where AI-powered analytics can be harnessed to uncover the key consumer behavioral patterns across regions and segments. The insightful nuggets can then be used to develop strong up-selling and cross-selling strategies, and eventually streamline the overall customer journey to drive growth and profitability.

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Authored by
NSEIT Data and analytics practice
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