How Data is Fueling Massive Changes in Financial Services
This won’t come as a shock, but data collection isn’t new. Companies and organizations have been gathering data about customers for hundreds of years. What is new are the myriad ways we now have available to us to collect all that data, the amount of data we’re gathering, and the ways in which we can use it.
The evolution of data collection
Over the last decade, we’ve seen the rise of “big data”—a shift from internal transaction-based data to the collection of, well, everything.
The Internet, coupled with ubiquitous smartphone usage, has facilitated massive amounts of new data that companies never had access to before. New hardware offerings have delivered “in memory” data analytics that dramatically accelerates the time it takes to solve complex business issues, while cloud-based analytics have given organizations massive computing power at low costs. Meanwhile, new frameworks like Hadoop, new scripting languages like Python, and databases like NoSQL have made it even easier to store, analyze, and manipulate all the data we could ever want.
Today, data drives better search algorithms, makes personalized recommendations to consumers, suggests similar items, and targets us with ads they already know will resonate. From smart watches to connected home devices like refrigerators, thermostats, and doorbells, to our vehicles, we’re producing—and companies are collecting—more data than at any point in human history.
Retailers now know what you’re buying, what websites you’re visiting, what vacations you’re planning, who you’re calling, and what stores you’re shopping at in real life.
“Over 2.5 quintillion bytes of data are created every single day, and it’s only going to grow from there. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth,” according to new figures published in an infographic by Domo.
There are few industries today that aren’t looking to data and analytics for deeper, more actionable insights about their customers. We’ve reached a critical point, not of “What do we know?,” but “What do we do with what we know?”
This same big data can have a dramatic effect on the financial services industry—but only if we find ways to draw new insights from it, much the same way Google, Facebook, Apple, and Amazon have in their respective industries.
Our reliance on simple data like the average age of a new homeowner isn’t even entry-level stuff anymore. We’re so far past that. We must use all this data—and the insights we can gain from it—to create better, more personalized customer experiences, more personalized marketing strategies, and stronger, more secure data and fraud prevention programs.
The Benefits Big Data Offers Financial Institutions
Financial Risk Management
Banks are able to use big data to enhance the accuracy of risk assessment and to improve lending practices.
We can gain valuable insights from demographic, geographic, and time-of-day data, revealing not just behaviors, but intentions. These are the insights that will move your financial organization beyond traditional lending scores. With the right data and analytics, you can even get insights into market and operational risks.
Fraudulent transactions already cost financial institutions billions of dollars a year. Juniper Research reports that “annual online payment fraud losses from eCommerce, airline ticketing, money transfer, and banking services, will reach $48 billion by 2023.”
As fraud becomes more prevalent—and those behind it brasher—the costs of fraud prevention is only going to rise. By identifying spending and deposit patterns, banks can use new data to identify fraudulent transactions or entries earlier, mitigating the cost of lost trust and lost dollars.
The days of identifying customers by age, sex, or marital status are far behind us. Today, banks can consider spending habits, debt patterns, speed of repayment, and other more granular details and trends to help determine which products are right for specific customers.
Rather than selling based on quotas, banks have an opportunity to analyze customer behavior to offer more personalized solutions—something customers already expect. According to research from Salesforce, “70% of customers say understanding how they use products and services is very important to winning their business.” Knowing your customer’s desires is now table stakes. The right data strategy will help you get there.
It is critical to use data in ways that show customers you value and appreciate them. Integrated, robust data and contextual intelligence can provide deeper marketing, sales, and service experiences—a growing trend known as “lifestyle banking.”
As mentioned, customers want highly personalized offers that go beyond basic data about them. They expect authentic interactions that span multiple channels. They expect you to understand their intentions, wants, needs, and to be able to offer products that match. In that same Salesforce research from earlier, “84% of customers say being treated like a person, not a number, is very important to winning their business.”
…But Not Without Challenges
No surprise here, but with increased access to customer data comes an increased demand for transparency and accountability. Financial institutions are facing stricter regulations around data collection and usage, meaning banks need to ensure compliance with laws and regulations like GDPR, PIPPEDA, and Basel II.
Increased data breaches, mishandling of data, and negligent behaviors are causing fear and distrust among consumers as well. Customers want clear expectations around how their data will be used and the collection methods they might be subject to.
Digital transformation poses a number of challenges for banks, but key among them is the breaking down of silos. Creating new systems and structures around how banks function and how information is shared is no easy task. But it’s a challenge financial institutions must face head-on.
Volume and Structure
Having lots of data is one thing; knowing how to clean it, organize it, and structure it for use is something completely different.
Legacy systems are unable to handle the volume, variety, and velocity of data flowing in. That’s why banks must embracing new hardware and software solutions that can manage high volumes and disparate sources of data.
Making Lemonade: Turning Challenges Into Opportunities
Data is at the core of innovation in the financial services industry. Knowing the many ways you can leverage data—coupling insights with AI or machine learning, for instance—is the single greatest benefit in front of you. With the proper strategies in place, you can unlock new and exciting opportunities.
Make an investment in a unified data ecosystem that can converge data for actual use. Start small, experiment often, go slow, and you’ll find new ways to apply data analytics in your digital transformation.
But whatever that solution looks like for you, make sure it’s flexible enough that you can store, manage, analyze, and apply that data with speed and reliability while also scaling in the future.