The Regulation that begs the question: How well do you really KNOW YOUR CUSTOMER?

As a consumer, I’ve experienced this firsthand. Once at a work conference, I charged the bulk of my expenses to my personal credit card. The bill came to about $20,000—which immediately triggered a fraud alert. By the time everything was resolved, I had to go through the entire verification process from step one. It was time consuming and irritating, especially during a busy conference. Worse yet, I knew that my credit card company already had all my data: It was just in too many silos to track down.

Silos that slow down

No matter the organization, data silos tend to appear in the same spots. The first is between products. For instance, a customer may have a mortgage, auto loan and small business loan with the same bank. Yet the customer’s data related to each product may be split into different departments—even though in reality, it exists as part of the same relationship. Now, imagine that this customer has a spouse who also uses the bank: the relationship would be even more fragmented.

Administrative data silos represent another common culprit. If the legal, compliance and credit departments of the same bank have separate files for the same customer, each department has an incomplete picture simply because operational groups fail to share the data. Administrative data silos are particularly difficult to avoid when customers or portfolios transfer between institutions or mergers and acquisitions take place. Vital data often goes lost during these processes, and legacy systems tend to communicate poorly with one another.

When data lives in so many places, it’s difficult for a financial institution to quickly assess who is on the other end of a transaction, what they likely need and how best to serve them. And when the know your customer process is slow, it can push valuable customers toward other lenders. Just as bad, the cost of the extra underwriting cuts directly into your bottom line.

The sad truth is that banks impose this obstacle on themselves—but they can remove it themselves as well. Use these three strategies to break down the data silos, integrate information and optimize KYC.

1) Use federated search

While full data integration is always a worthy pursuit, the difficulty and costs may be too prohibitive for some financial institutions. Luckily, federated search essentially automates the integration process. Any time data is pulled from a stack, a parallel record is created in a common stack and over time, a unified repository of the most important data develops.

More than three quarters of executives believe information is a mission-critical asset, yet 60 percent also say it’s too hard to find. Federated search creates a single source of truth, increasing both accuracy and speed. When everyone works with the same information, signals don’t get crossed and employees save time because they can grasp a full picture without accessing multiple systems.

2) Build a robust platform with robotic process automation (RPA)

As a whole, nearly 10 percent of financial institutions spend more than $100 million annually on KYC. By using robotic process automation, banks can automate some traditionally manual tasks, which saves money and reduces the likelihood of error.

RPA uses software to carry out rule-based functions based on data from internal and external sources and inserts the results directly into workflows. That’s especially helpful when you compile customer information scattered across systems. For example, if a customer is on the phone with a representative from the dispute resolution team and then needs to be transferred to sales, RPA can skip a second round of KYC after the transfer. It can also enable the sales representative to immediately understand what transpired earlier on the call, so everyone lands on the same page with minimal friction.

More than 64 percent of organizations surveyed by Deloitte have started to use RPA, but only 3 percent have scaled their digital workforce; as a result, KYC still suffers. Support for these initiatives usually comes from the top down, so we should see a stronger push in the direction of RPA as the technology improves, implementation simplifies and more business leaders prioritize the transition.

3) Experiment with synthetic underwriting

You can’t integrate data you don’t have but you can take certain public data and make plausible assumptions that fill customer profile gaps. This is exactly what synthetic underwriting does. You don’t have this information until you begin the underwriting process but it doesn’t take long to track it down if you use the right technology.

Synthetic underwriting doesn’t just give you a speedier underwriting process; it also affords far greater decision-making power. Intelligent automated data gathering can supplement traditional underwriting, mitigate risk and even empower your marketing team with critical information for future targeted campaigns.

Banks that properly leverage data can revolutionize the financial services industry, as long as they can break data free from silos. Split-up data loses its value, offering half-truths instead of hard facts. It’s far from ideal for banks or their customers.

But it is possible to fix. Once the silos vanish, something new appears: information that delivers confidence and clarity when it matters most.