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  • Writer's pictureBhagyeshwari Chauhan

Big Data in Banking: 6 Ways Banks can Leverage Big Data Analytics in Financial Services

Updated: Feb 12, 2021

Banks are the touch points that drive our business. Over the past years, the banking industry has grown by leaps and bounds in terms of their operations, delivery systems and services. However, the banking industry is at yet another point where it’s about to change its scope forever. Due to rise in big data technologies and the huge volume of data collected and analyzed, experts believe that harnessing insights from big data in banking will propel the banking industry into the 21st century.

In this day and age, there is not a single person who doesn’t have a mobile banking or payments related app on his/her smartphone. Imagine the amount of data generated each time someone makes a payment or even checks his or her balance.

Big Data in Banking: How can big data be leveraged into better insights by the banking industry?


According to Forbes, 87% of the industries think that incorporating big data insights will be the huge boost they have been looking for. To add to that, not having a very strong big data can cause companies to fall behind in the race.

There is a humongous amount of data generated daily by every industry. But those who are able to harness all that it has to offer can really become the game changers of their markets.

Here are 6 changes big data in banking can bring about in the current state of banking operations:

  • Fraud Detection

Fraud detection is one of the most important uses of big data in banking which can be used to differentiate legitimate business transactions from fraudulent transactions. By analyzing their customer’s transaction history and spending patterns, savings, investments income sources once can detect any unusual activity which might be an indication of a fraud. Thereafter immediate actions such as blocking irregular transactions might prevent the fraud before it occurs.

  • Compliance and Regulatory Requirements

Financial services require a heavy level of monitoring and reporting and operate under the quite a heavy framework. The Dodd-Frank Act, which was enacted after the 2008 financial crisis states that deal with monitoring and reporting of every trade is required. This data is then used for travel surveillance which recognizes trading patterns.

Banks now have access to their customer’s needs. Cloud-based analytics can now sync with your big data systems, creating actionable insights dynamically. Now banks are capable of providing their customers with the exact services they are looking for.

  • Customer Segmentation

For a long time now, banks have faced the pressure for switching their way of working from product-centric to customer-centric. One way to do this through understanding their customers better by segmenting them. With the help of big data, it is quite easy to divide their customers into distinct segments based on demographics, daily transactions, interactions with online and telephonic customer services and external data such as the value of their homes.

  • Personalized Marketing

Going above and beyond segment-based marketing is personalized marketing. Customers with similar needs and buying habits are targeted by one ad campaign, instead of making a general campaign for everyone.

While most of the data is based on the analysis of merchant records, firms can also go ahead and take data from various other sources like social media records to fully understand a customer’s needs and wants.

  • Risk Management

Every firm needs to prepare for disasters. But the need for such is highest in the finance industry. Regulatory schemes such as Base III require firms to manage market liquidity risk through stress testing. Banks may also manage and reduce the risk by assessing customer profiles. The risk of algorithm trading is managed thought back-testing strategies with historical data. Big data can

also support real-time risk altering if a risk threshold is surpassed.

  • Change in delivery system

Big data may seem like it’s a huge ecosystem of its own, but its job actually is to simplify tasks. Whenever a name or account number is entered into the system, it quickly sifts through all the data and only provides the required info. These ways, banks can streamline the process and make their work more efficient and quick.

More than 25% of the firms have already started implementing insights gained from big data in banking and have a huge competitive edge in the market.

Looking to jump up the finance ladder using big data insights in banking? Contact us at Datahut, your big data experts. 

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