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April 7, 2020 - Priya Rajan

Balancing Customer Experience and Fraud Prevention Across the Entire Customer Lifecycle

Learn how your organization can deliver consistent omnichannel experiences for your customers while increasing fraud detection accuracy using a new approach to data intelligence.

The concept of complete customer lifecycle protection represents the culmination of parallel evolutions in the areas of customer service and fraud prevention. Today, forward-thinking organizations are embracing centralized intelligence as an effective means to balance customer experience and risk management. They are integrating and deploying advanced AI and machine learning-powered systems that enable holistic analysis and ensure consistent coverage and real-time responsiveness. 

It wasn’t always this way.

The Decentralized Silo Era

On the customer service side, there was a time when enterprises were predominantly acquisition-oriented. A financial institution’s focus, for example, would have been on getting a customer in the door to open a new checking or savings account, or sign up for a new credit card, or apply for a new loan. Once processed, new customers would then go about their business largely without ongoing, meaningful, and informed engagement with the organization. Each side suffers when this occurs. Customers do not receive the highest levels of personalized treatment, and businesses don’t reap the full value of customer loyalty as a result of having not understood the full scope of customer interactions across all products, services, and touchpoints.

Often, customers get “passed” from team to team, depending on what issues or incidents might arise during their customer journey. One team might help manage changes in account details, another team might help with the opening of an additional account, and still another team might help with a fraudulent transaction. From the customer side, this is a recipe for friction. As an example, if a customer provides an updated address to their mortgage loan officer, they expect other departments to have that information as well.

The result of this decentralized approach is the emergence of data silos—disconnected bodies of data not “talking” to one another. The problem of silos has become exponentially worse with the expansions of the big data era.

Similar mindsets and processes have dominated fraud prevention as well. Even as organizations have begun to build new infrastructure and data lakes to manage rapidly growing volumes of data, lack of data-sharing across different business units continues to be a problem. Teams continued to be unable—or unwilling—to share. Within a financial institution, for example, individual business units maintain ownership over the data they collect. The creation of a collective repository can introduce questions of security as well as data reconciliation, both of which are challenging issues to address. 

Ultimately, creating data lakes and data repositories to make data reading accessible and usable represent steps forward, but they are not the ultimate goal. Comprehensive fraud prevention today requires that we evolve from centralized data to centralized intelligence; meaning, intelligence that is accessible and adaptable to any business scenario.

The Big Data Era

As our economy has become increasingly digitized and data-driven, the realities of the customer lifecycle have come into increasingly vivid focus, with businesses across industries realizing the importance of growing a long-term customer base, and the opportunities and challenges associated with that effort. Above all else, it has become abundantly clear that businesses need to consistently engage with their customers across their entire lifecycle—not just at the beginning, and not on an as-needs basis. Evolving customer expectations demand it, and fraud and risk management requires it.

The matter of customer experience is particularly critical, given how significantly consumer expectations have changed. Today, customers expect their experiences to feel like they’re engaging with a business that “knows” them. New insights from Deloitte, published in a piece titled “Accelerating digital transformation in banking,” make clear this new reality. A key piece of advice the report offers? “Break the channel silos”:

“Consumers’ fascination for omnichannel experiences is real. Seventy percent of consumers in our study consider a consistent experience across channels to be extremely important or very important in selecting their primary bank. Therefore, banks must have a seamless flow of data across all channels. Having a 360-degree view of consumer interactions across channels, products, and systems will pay off by building stickier emotional engagement.”

The Challenges of Balancing Customer Experience and Risk Management

While awareness around customer lifecycle management has increased, the ability to deliver consistent omnichannel experiences has remained a problem. A central issue has been the challenge of balancing customer experience with risk management. 

On the one side, businesses must contend with the ever-increasing pressure to deliver seamless customer experiences that prioritize speed, efficiency, and accessibility; an evolution that has taken place simultaneous to increasing pressure around privacy and data acquisition, with both regulators and customers demanding greater transparency and more ethical practices. On the other side, businesses must deal with the increasing sophistication, speed, and scale of modern digital fraud. Armed with the latest tools and technologies, a steady stream of breached data, and the power of massive bot armies at their disposal, modern fraudsters are launching attacks of unprecedented complexity and scope. 

These are real challenges. Fortunately, a centralized intelligence-based model that leverages the power of AI and machine learning, and which focuses on the entire customer lifecycle, can offer a solution. 

The Centralized Intelligence Solution

In a centralized intelligence model, actionable insights derived from data are centrally and directly available across a system, and throughout an organization. At DataVisor, one way we make this possible is through our new Feature Platform. Feature Platform is a centralized hub that enables all users to create signals and features from big data, and then subsequently manage these features across teams. 

From the moment new customers engage with—and are onboarded by—an organization, helpful information about these customers is acquired. By ensuring relevant information is holistically analyzed, contextualized, and centralized in real time, it becomes possible for a business to simultaneously protect and serve its customers throughout their entire journey.

Forward-thinking enterprises understand that customer experience and fraud prevention are not separate issues. They are interwoven, and inseparable. Our clients know that comprehensive fraud protection is, in fact, one of the most customer-centric benefits a business can offer its customers.

To learn more about DataVisor’s approach to Centralized Intelligence, download our most recent guide here.

Photo of Priya Rajan
about Priya Rajan
Priya Rajan is VP of Marketing at DataVisor. She is a highly-regarded leader in the technology and payments sectors, bringing more than two decades of experience to her role. She has previously held leadership roles with high-growth technology organizations such as VISA and Cisco, and Silicon Valley unicorns like Nutanix and Adaptive Insights.
Photo of Priya Rajan
about Priya Rajan
Priya Rajan is VP of Marketing at DataVisor. She is a highly-regarded leader in the technology and payments sectors, bringing more than two decades of experience to her role. She has previously held leadership roles with high-growth technology organizations such as VISA and Cisco, and Silicon Valley unicorns like Nutanix and Adaptive Insights.