Data has become so widespread across commerce and financial services that its presence alone no longer confers any distinction. What matters now is whether companies can convert that information into decisions that shape outcomes in real time.
Hal Lonas, chief technology officer at Trulioo, told PYMNTS that most firms recognize the importance of data, but fewer understand the effort required to make it useful.
“All companies now who are paying attention see that they can use data to their advantage,” he said. “There’s probably a bit of a misunderstanding on how hard it is or how much dedicated effort you have to put into … mak[ing] it useful.”
The environment surrounding data has shifted quickly, in part because of advances in artificial intelligence and new analytical tools. That pace has created uncertainty rather than clarity in many organizations.
Lonas said companies now face a moving target. “One of the impediments actually to companies using their data is how rapidly the data utility environment is evolving,” he said, as part of the “What’s Next in Payments” series titled “The Data Game.” Firms hear about large language models and AI agents, but often hesitate because the path forward is not clear.
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Hidden Cost of Idle Data
Most companies, Lonas said, fall into a pattern of gathering information and leaving it largely untouched. That approach does more than limit potential upside. It introduces new forms of exposure.
“The majority that get that data, keep the data,” he said adding that these firms are “creating a problem for themselves in terms of surface area that is susceptible to attack or stealing of the data.”
In effect, organizations assume the risks associated with holding sensitive information while failing to extract the operational benefit. The result is an unfavorable position where exposure rises but performance does not improve.
Fraud Expands as Surface Area Grows
Fraudsters have adapted accordingly. Lonas described an environment in which bad actors increasingly mimic legitimate behavior. “Most users are good,” he said, but fraudsters “are getting better all the time at pretending to be real users.”
This includes not only individual identity fraud but also more complex schemes involving businesses. Shell companies and layered ownership structures obscure accountability, making it harder to assess risk.
The challenge for companies lies in identifying fraud without disrupting legitimate activity. Excessive controls can drive away customers who expect a straightforward experience.
Lonas described this as a careful balance. Companies must “employ those techniques without imposing so much friction on the process or the end user that they just decide that it’s easier to do business somewhere else.” This requires a more precise application of controls. Rather than treating all transactions equally, firms must evaluate risk in context and respond accordingly.
Dynamic friction becomes central to that effort. Higher-risk scenarios justify additional verification, while lower-risk interactions should proceed with minimal interruption. The objective is to allow legitimate transactions to move forward while isolating suspicious behavior.
What Trulioo Sees in Practice
According to Lonas, companies that apply data effectively are already seeing measurable gains. In underwriting and approvals, more advanced use of data and analytics has accelerated processes that once required extensive manual review.
“Customers who are employing the most cutting-edge analytics and technologies are seeing a 10x improvement in onboarding times, approval times, credit decisioning,” he told PYMNTS.
At the same time, these firms are better positioned to respond to evolving fraud tactics, including synthetic identities and increasingly sophisticated business fraud schemes.
Winning the data game, Lonas said, comes down to action rather than accumulation. Firms that treat data as an operational asset, apply it in real time and calibrate their controls stand to gain.
“To win the data game is to use your data,” he said. “If you’re just sitting on the data, you’re just not using it to your advantage.”
