5 Ways Advanced Analytics is Transforming the Banking Industry
The banking industry is going through a transformation requiring banks to make data and analytics an integral part of their processes and workflows.
This transformation is driven in part by emerging fintech providers, who are fully digital and built on top of cutting-edge technologies and analytics. At the same time, consumers are increasingly using mobile and online banking services. One study, for example, found that 89 percent of consumers use mobile banking, and 70 percent said mobile banking is now their primary method for accessing their accounts. Business Insider predicts that the digital-only bank—or neobank—market is poised for significant growth, a trend that will be fueled largely by millennials who are frustrated with legacy financial providers.
In turn, digital disruptors and tech-savvy customers are putting pressure on traditional banks to modernize and embrace data-driven strategies.
Right now, though, the banking industry is still behind the curve, as 92 of the top 100 global banks are still running on legacy IBM mainframes. While IBM mainframes are useful for processing large volumes of transactions, they’re less helpful for managing big data.
It’s clear that yesterday’s systems and processes are no longer capable of serving digital consumers. As we move deeper into the digital era, the need to modernize will become even more important. This is why an increasing number of banking institutions are turning to big data analytics to figure out the best path forward.
After the initial big data explosion more than a decade ago, companies started racing to collect data despite lacking the tools for storing and processing it—which made it very difficult to discover key insights. This problem has gotten worse over time, too, as data has grown in both volume and complexity. Today, around 2.5 quintillion bytes are created every day—a figure that’s certain to keep growing larger as we move further into the future.
To improve data management, a growing number of companies are turning to big data analytics solutions that prepare, process, and examine large data sets. In fact, big data analytics in the banking market is expected to grow at a 12.7 percent CAGR through 2025, with the market on pace to exceed $62 billion by then.
How Advanced Analytics is Redefining Banking
Thanks to advanced analytics in banking, financial organizations are now able to act with far greater agility than they could in the past. At the same time, with analytics, it’s becoming much easier to predict future market conditions and emerging customer trends.
With that in mind, here are some of the ways banks are using data analytics to grow during this period of transformation.
1. Financial Inclusion of Risk Assessment
The proliferation of cloud-based analytics software has made it possible to quickly analyze very large datasets. As a result, financial companies can use more data when assessing risk such as that associated with distributing loans, trading, and acquiring new businesses.
One trend that has grown significantly is financial inclusion, which involves providing financial products and services to vulnerable or underserved groups, which are presumably riskier decisions than those for traditional bank customers. According to the World Bank, 69 percent of adults—or 3.8 billion people—now have an account with a bank or mobile financial provider. This is an increase from 62 percent in 2014 and 51 percent in 2011. Suffice it to say that new technologies and improvements in analytics are making it easier for companies to provide financial services for people with poor credit scores and employment lapses.
“In the past few years, we have seen great strides around the world in connecting people to formal financial services,” explains World Bank President Jim Yong Kim. “Financial inclusion allows people to save for family needs, borrow to support a business, or build a cushion against an emergency. Having access to financial services is a critical step towards reducing both poverty and inequality, and new data on mobile phone ownership and internet access show unprecedented opportunities to use technology to achieve universal financial inclusion.”
2. Improving Customer Loyalty by Personalizing their Experience
Banks are now using big data to gain deeper visibility into customer interactions, market and economic conditions, and competitors’ actions. By combining metrics from all of these categories, it’s possible to engage with customers in a highly personalized manner.
For example, a bank may study loan distributions over time and discover when consumers are most likely to accept an offer. By sending targeted offers at the right time, banks can increase their odds of closing deals. Decisions around cross-selling and up-selling bank products to customers is also driven by predictive analytics that look at various ‘signals’ simultaneously such as changes in spending patterns or visits to a local branch to determine which product to offer, what promotion to associate with it and what’s the ideal time to engage a customer with such offerings.
3. Optimizing Investments to Drive Business Growth
Banks are also using data analytics to make investment decisions. The volume and variety of data available can truly help banks make smart investment decisions around expansions and new product offerings during this transformational time for the industry.
For example, consider a bank that’s trying to open a brick-and-mortar branch in a new market. The bank now has the ability to use a data-driven approach to study a variety of factors such as local demographics, average household income and education level, along with transaction and product usage data of existing customers in that location to gain a better understanding of the magnitude of business it would generate from the surrounding community over time. This information could help avoid launching an unprofitable operation.
4. Improving the Customer Experience (CX), while delivering on Security
Banks are constantly challenged when it comes to improving CX while deploying security measures to protect their customers’ information and identities. Financial cybercrime has increased by over 300 percent during the last five years, making it a top challenge for the industry.
It isn’t easy to make the tradeoff between security and customer experience. Provide too few security options, and hackers will be more likely to breach sensitive accounts. Provide too many security options—like passwords, security questions, and multi-factor authentication—and customers will likely get annoyed to the point they may even leave altogether. Seamless experiences are table stakes for any product these days.
Banks are increasingly using real-time analytics and artificial intelligence (AI) on transaction data to deploy sophisticated security mechanisms. It is not uncommon, for example, to receive a text message when you try to make a transaction at a location or store outside of your usual spending pattern. Not only is the notification real-time, but it also lets you respond and authorize the transaction within seconds instead of having the transaction declined and requiring you to call the bank to get it authorized.
Banks can use an SIEM (Security Information and Event Management) system to detect, for example, account activity from a country where they do not do business. This system can trigger a fraud alert, enabling security teams to take action and prevent abuse.
5. Increasing Operating Efficiency
Busy financial organizations are constantly looking for ways to increase productivity in order to save time and improve output. After all, time is money in the ultra-competitive space of commercial banking—where customers are often engaging with several competitors at any given time for things like credit line increases, loan approvals, and opening savings accounts.
Using data analytics, banks can process such transactions at a much faster clip. This can lead to more deals getting closed and greater returns.
Bringing It All Together with Neebo
Being in an industry that is getting transformed, are you and your team receiving more business questions that require deep analytics with short turnaround times? Do you grapple with data silos and a lack of tools that can bridge those silos and foster collaboration among analytics team members?
Neebo helps analytics teams discover and merge data from multiple disparate databases, making it possible to discover insights that would otherwise go undetected. With robust integrations with a wide range of data sources and Business Intelligence and Data Science tools that your team relies on every day, Neebo serves as a virtual analytics hub that can take you from Data to Insights faster than ever.
Learn how Neebo can increase your analytics team’s productivity by Scheduling a 1:1 Demo.