By: Natalie Johnson
The most effective lending strategies depend on how well lenders and clients collaborate around timely, relevant information to make better decisions together. For Jonathan Telzrow, Director Credit Solutions at TransUnion, a future-proof commercial lending strategy rests on a simple principle that better data creates better conversations, and better conversations lead to smarter financing outcomes.
“All a lender does is sell money,” says Telzrow. This creates a significant challenge. However, when the core product is largely the same across the industry, differentiation comes from the quality of insight that supports underwriting decisions, credit assessment, and client partnerships. The institutions that excel will be those that transform credit intelligence into meaningful action.
Turning Credit Intelligence Into Competitive Advantage
Lenders have access to more information than ever before, but data alone does not create value. The real advantage comes from translating risk analytics into actionable insights that improve both lender performance and borrower outcomes. Data-driven lending requires continuous evaluation before and after a credit decision is made. “The analytics don’t stop. It’s a pre and post,” Telzrow explains. “All you’re trying to do is compress that timeline and use the data to make better decision-making.”
Before extending credit, lenders use analytics to identify opportunities, evaluate risk, and determine pricing. Afterward, they assess whether those decisions delivered the expected results. This approach supports smarter lending through analytics by creating a feedback loop that strengthens future underwriting decisions and helps lenders refine their decision frameworks, making credit extension decisions more accurate over time.
Why Recency and Relevance Matter Most
While effective lending collaboration depends on transparency, not all data carries equal weight. Telzrow points to what he calls the “two Rs”: recency and relevance. “I’m looking for that data now. That’s new, that’s fresh, that’s giving me the current perspective of someone,” he says. “I’m not looking at how they were paying their mortgage seven years ago. I’m looking for how they are paying their mortgage now.” Historical data remains valuable, but real-world financial conditions can change rapidly. Decisions based on outdated information may fail to reflect a borrower’s present situation.
Relevance is equally critical. Data should directly support the question being asked. Whether evaluating merchant financing opportunities, commercial expansion plans, or portfolio management strategies, lenders must ensure that every data source contributes meaningful context to the decision. Traditional credit files, trended credit data, and alternative data all have a role to play. The challenge is determining which information strengthens credit analytics for informed decisions and which merely adds noise.
Building Faster, Stronger Lending Relationships
Speed has become one of the defining characteristics of modern lending collaboration. Businesses seeking financing often evaluate multiple options simultaneously, creating a narrow window for engagement. “Time to market is everything,” Telzrow says. “You’re looking for signals in the marketplace of this commercial entity or this consumer is looking for credit right now.”
Enhancing lender-client collaboration increasingly means identifying those signals and responding quickly with relevant financing solutions. This requires more than advanced technology. It demands alignment between data providers, lenders, and internal operational systems.
Data-driven credit decision-making enables lenders to assess opportunities rapidly, but execution remains critical. Institutions must be capable of delivering the right offer through the right channel at the right moment. Whether communication occurs through digital platforms, email, social media, or traditional channels, responsiveness has become a competitive differentiator.
The Expanding Role of AI and Advanced Underwriting Frameworks
Over the next several years, artificial intelligence (AI) is expected to play a larger role in commercial lending strategy. Telzrow believes the most successful applications will enhance human expertise. “The power of AI is really in bringing people’s talents to bear quicker,” he says. Used effectively, AI can strengthen credit intelligence by identifying emerging risks, uncovering new lending opportunities, and improving portfolio management.
It can also support advanced underwriting frameworks that provide a more complete view of borrower behavior through traditional, trended, and alternative data sources. At the same time, lenders must balance innovation with fairness and compliance. Established credit reporting standards remain essential foundations for responsible decision-making, even as new data sources become available.
Collaboration Will Define the Future of Financing
More than half of lenders are already adopting sophisticated data-driven approaches, and the pace of adoption continues to accelerate. For lenders, the implications extend beyond operational efficiency. Better lending collaboration produces stronger borrower relationships, more informed underwriting decisions, and healthier portfolios. For clients, it creates access to financing solutions that more accurately reflect their current circumstances and future potential.
“If you’re not comparing, you’re really just taking too many shots in the dark,” Telzrow says. Success is increasingly determined not by how much data an institution possesses, but by how effectively it transforms that information into insight, action, and collaboration. As credit markets become more competitive, the ability to build stronger lending relationships through data-driven lending may prove to be the industry’s most valuable differentiator.
Follow Jonathan Telzrow on LinkedIn or visit Jonathan Telzrow’s website.
