5 Ways Advanced Banking Analytics is Transforming the Industry

The banking industry is going through a transformation requiring banks to make data and analytics an integral part of their processes and workflows. Banking analytics!

This transformation is driven in part by emerging fintech providers, who are entirely digital and built on top of cutting-edge technologies and analytics. At the same time, consumers are increasingly using mobile and online banking services. For example, one study 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 neo bank —market is poised for significant growth. This trend will be fueled mostly 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.

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 critical. It is why an increasing number of banking institutions turn 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 to store and process it, making it very difficult to discover critical insights. This problem has gotten worse over time, too, as data has grown in 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. Big data analytics in the banking market are expected to grow at a  12.7 percent CAGR  through 2025, with the market on pace to exceed $62 billion by then. 

How Advanced Banking Analytics is Redefining the Industry

Thanks to advanced banking analytics, financial organizations can now act with far greater agility than they could in the past. Simultaneously, with analytics, it’s becoming much more comfortable 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 analyze vast datasets quickly. As a result, financial companies can use more data when assessing risk, such as 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 own an account with a bank or mobile financial provider. It is an increase from 62 percent in 2014 and 51 percent in 2011. Suffice it to say that new technologies and improvements in analytics make it easier for companies to provide financial services for people with low 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. Combining metrics from all of these categories makes it 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 are driven by predictive analytics. The process looks 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 can now use a data-driven approach to study various 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 better understand 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 continuously 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 approved.

Banks can use a 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 continually looking for ways to increase productivity 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. It can lead to more deals getting closed and greater returns.

Easy Data Transformation is the Key

Effective financial services analytics starts and ends with clean, organized, and processed data.  How you transform your data is critical to this, in terms of both process and how.  Analytics are assembled by analyzing complex and diverse datasets that need to be cleansed, blended, and shaped into final form.  Often times this involves high degrees of collaboration between data engineering and analytics teams.  It also requires rich data documentation to back up compliance processes.

Datameer is a powerful SaaS data transformation platform that runs in Snowflake – your modern, scalable cloud data warehouse – that combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics.  With Datameer, you can:

• Allow your non-technical analytics team members to work with your complex data without the need to write code using Datameer’s no-code and low-code data transformation interfaces,
• Collaborate amongst technical and non-technical team members to build data models and the data transformation flows to fulfill these models, each using their skills and knowledge
• Fully enrich analytics datasets to add even more flavor to your analysis using the diverse array of graphical formulas and functions,
• Generate rich documentation and add user-supplied attributes, comments, tags, and more to share searchable knowledge about your data across the entire analytics community,
• Use the catalog-like documentation features to crowd-source your data governance processes for greater data democratization and data literacy,
• Maintain full audit trails of how data is transformed and used by the community to further enable your governance and compliance processes,
• Deploy and execute data transformation models directly in Snowflake to gain the scalability your need over your large volumes of data while keeping compute and storage costs low.

Learn more about our innovative SaaS data transformation solution, Sign up for your free trial today!