Marketing and credit risk have traditionally operated in parallel, sharing goals but not always sharing the same decision-making framework. As lenders face increasing pressure to grow portfolios while maintaining disciplined risk controls, the gap between acquisition strategies and underwriting decisions is hard to ignore.
For Jonathan Telzrow, Director Credit Solutions at TransUnion, the answer lies in a more integrated approach to credit intelligence. “The biggest missed opportunity is looking for additional data and finding the additional data that could be alternative data, which could be trended type data from traditional credit sources.” The most effective lenders are identifying the data that meaningfully improves outcomes and using it to drive faster, more accurate decisions.
Moving Beyond Data Accumulation
The financial services industry has no shortage of data. New vendors, alternative datasets, and predictive models continue to emerge, promising sharper insights and stronger performance. Yet more information does not automatically lead to better results. “There’s a rush right now in the marketplace to just consume more data, to buy more data,” says Telzrow. “But ultimately everything comes with a cost.”
Collecting, storing and using more data requires stronger decision-making frameworks built around measurable value. The objective is not data accumulation but data-driven lending. Whether evaluating merchant financing opportunities, commercial borrowers, or consumer portfolios, institutions must understand which variables contribute to smarter lending through analytics and which simply add complexity.
Why Timing Matters as Much as Insight
Even the most sophisticated credit intelligence loses value if it is not acted upon quickly. One of the most significant advances in modern commercial lending strategy is the ability to access real-time credit information and connect it directly to acquisition efforts. “When you’re able to see real time, you’re able to see the customer at this point of time,” he says. “If the client or the prospective client is in the market for new credit, they want it now and they’re going to look for credit now.”
This principle applies across both consumer and commercial markets. Real-time visibility combined with rapid execution creates a more accurate picture of borrower needs while improving conversion opportunities. Data-driven credit decision-making is most effective when lenders can compress the timeline between analysis and action. Success increasingly depends on “making the right offer to the right person at the right time,” creating a stronger foundation for optimizing credit extension decisions.
Aligning Risk Appetite With Market Strategy
Not every lender should pursue the same customer segments, and attempting to serve every borrower profile often creates inefficiencies. As credit markets continue to separate between super-prime and stressed borrowers, marketing and underwriting teams must establish clear alignment around risk tolerance. Lending collaboration becomes essential because acquisition goals cannot be separated from risk objectives. “You’re not going to be everything to everybody,” Telzrow says. “It’s extremely hard to do that. I would argue damn near impossible.”
For super-prime customers, product features, pricing, and convenience may drive engagement. For borrowers actively seeking access to credit, timing and availability often matter more. Understanding these distinctions helps institutions develop stronger client partnerships while ensuring marketing investments support broader portfolio goals. Rather than chasing every opportunity, lenders can focus resources on the segments that align with their risk appetite and long-term growth strategy.
Building Infrastructure for Better Decisions
Technology infrastructure remains one of the most overlooked barriers to enhancing lender-client collaboration. Many organizations discover promising new datasets only to learn that their existing systems cannot ingest or operationalize them. “It’s one thing to say, ‘I want that data,'” he notes. “But if I can’t use it, what’s the point?” This challenge becomes even more important as institutions combine traditional credit files with trended and alternative data sources. Advanced underwriting frameworks depend on the ability to integrate information efficiently and generate actionable insights.
Telzrow sees significant value in pairing trended credit data with alternative sources to better understand borrower behavior over time. Whether a business is improving its financial position or showing signs of stress, these patterns help inform underwriting decisions and strengthen credit analytics for informed decisions.
The Future of Collaborative Financing
Lenders that prioritize relevant data, real-time execution, and integrated decision-making processes will be better positioned to manage credit risk effectively while identifying growth opportunities. At its core, the objective is to provide lenders and borrowers with the information needed to make confident decisions. By combining credit intelligence with effective lending collaboration, institutions can build stronger lending relationships, improve portfolio performance, and create more sustainable growth strategies.
