Easternpeak.com - Data is known to be one of the most valuable assets a business can have. Yet, it’s not the data itself that matters. It’s what you do with it. To spark your creativity, here are some examples of big data applications in banking.

1) Personalized customer experience

According to Oracle, 84% of the surveyed executives agree that customers are looking for a more individualized, tailored experience. The report also states that the ability to offer users what they need can bring you up to an 18% higher annual revenue.

Just like other businesses across a number of domains, banks use big data to get to know their users and, as a result, find new ways to cater to them, connect in a more meaningful way, and deliver more value.

Your data can give you valuable insights into user behavior and help you optimize your customer experience accordingly. For example, by having a complete customer profile and exhaustive data on product engagement at hand, you can predict and prevent churn.

This approach is reportedly used at American Express. The company’s Australian branch relies on sophisticated predictive models to forecast and prevent customer churn.

By analyzing the data about previous transactions (as well as 115 other variables), they can identify accounts that are most likely to close within the next couple of months. As a result, the organization can take preventive actions and keep their customers from churning.

 2) User segmentation and targeting

McKinsey finds that using data to make better decisions can save up to 15-20% of your marketing budget. Taking into account that banks spend on average 8% of their overall budgets on marketing, tapping into big data sounds like a great opportunity to not only save, but generate additional revenue through highly targeted marketing strategies.

By using big data, you can better understand your customers’ needs, pinpoint problems in your product targeting and find the best way to fix existing problems.

For example, Barclays has been using the so-called “social listening”, i.e. sentiment analysis, to source actionable insights from user activity on social networks.

When the company launched its mobile app, many people were unhappy with the fact that users under 18 were unable to transfer or receive money. The dissatisfied customers reacted by voicing their disappointment on social media.

As soon as the data collected by Barclays revealed the problem, the company was able to fix the issue by allowing users aged 16+ to access the app’s full capabilities.

 3) Business process optimization and automation

Further research from McKinsey reveals that around 30% of all work in banks can be automated through technology, and the key to this lies in big data.

As a result of advanced automation, banks can experience significant cost savings and reduce the risk of failure by eliminating the human factor from some critical processes.

JP Morgan Chase & Co. is one of the automation pioneers in the banking services industry. The company currently employs several artificial intelligence and machine learning programs to optimize some of their processes, including algorithmic trading and commercial-loan agreements interpretation.

One of its programs, called LOXM, relies on historical data drawn from billions of transactions enabling them to trade equities “at maximum speed and at optimal prices”, reports Business Insider. The process has proven to be far more efficient than both manual and the automated trading used earlier, and resulted in significant savings for the company.

Another data-based automation initiative from JP Morgan Chase is known as COIN. The machine learning algorithm, powered by the company’s private cloud network, is used to reduce the time needed to review documents: this task which previously required about 360,000 hours of work, now takes just a few seconds to complete.

The program also significantly decreased the human error associated with loan-servicing.

 4) Improved cybersecurity and risk management

On top of optimizing its internal processes, as mentioned above, JP Morgan Chase relies on big data and AI to identify fraud and prevent terrorist activities among its own employees. The bank processes vast amounts of data to identify individual behavior patterns and reveal potential risks.

Another leading financial service provider, CitiBank, is also betting big on big data technologies. The company is investing in promising startups and is establishing partnerships with tech companies as a part of its initiative called Citi Ventures. Cybersecurity is one of the major spheres of interest the company has been exploring recently.

As a part of this strategic move, CitiBank invested in Feedzai, a data science company that uses real-time machine learning and predictive modeling to analyze big data to pinpoint fraudulent behavior and minimize financial risk for online banking providers.

As a result, CitiBank can spot any suspicious transactions, e.g. incorrect or unusual charges, and promptly notify users about them. Apart from being useful for consumers, the service also helps payment providers and retailers monitor all financial activity and identify threats related to their business.

5) Better employee performance and management

Big data solutions in banking allow companies to collect, make sense of and share branch (as well as individual employee) performance metrics across departments in real time. This means better visibility into the day-to-day operations and an elevated ability to proactively solve any issues.

A global banking provider, BNP Paribas, collects and analyzes data on its branch productivity to identify and swiftly fix existing problems in real time.

Using the company’s data analytics software, branch managers, as well as chief executives, can get a birds-eye-view on the branch’s performance based on a number of metrics, i.e. customer acquisition and retention, employee efficiency and turnover, etc.

The future of big data in banking looks bright: Make sure to keep up

As you can see, there are many examples of how big data is used in banking. Yet, all those attempts have barely scratched the surface. The maximum potential of big data in banking is still to be harnessed.

According to the whitepaper by Global Transaction Banking, 62% of banks agree that big data is critical to their success. Yet, only 29% of them report getting enough business value from their data.

Banks need to rethink their operations and adopt data-driven approaches if they want to stay relevant and competitive. Plus, big data in the banking sector can help you improve and grow your business.