Once seen simply as a tool to control exposure and reduce risk, credit decision analytics have evolved into a sophisticated, data-driven approach that empowers smarter lending decisions. For institutions that get it right, analytics are proving to be a real source of competitive strength. “It’s easy to get hung up on using credit data to manage risk,” says Jonathan Telzrow, a seasoned expert in consumer and commercial payments. “But analytics can also identify where growth opportunities exist — who’s ready for more credit, where to position your offering, and how to bring those prospects into your organization.”
Telzrow, who has more than 25 years of banking and payments experience and has served on advisory boards for MasterCard, Experian, Discover, and FIS, sees analytics as a strategic driver across every stage of the lending cycle. “Growth is also in growing receivables and limiting losses,” he says. “The importance of analytics lies in identifying how to prospect, acquire, manage portfolios, and limit losses. It’s difficult to have one tool that does it all, so analytics should be applied across each step and then brought together into a unified roadmap.”
Using Data to Raise Approvals Without Lowering Standards
Raising approval rates while maintaining portfolio quality remains one of the toughest challenges for lenders. Telzrow argues that the key is moving from intuition to insight. “It’s no longer enough to rely on long-standing relationships or gut instinct.”
Data-driven precision starts with identifying the right applicants then ensuring they’re guided through the credit process effectively. “You can’t just target affluent demographics and claim success because approval rates are high,” he says. “You need a balanced, holistic approach.”
Each phase of the lending journey builds on the last. Prospecting feeds acquisition, acquisition feeds portfolio management, and portfolio management feeds loss mitigation. “When you manage all four steps effectively, you get the ultimate goal — a strong, performing portfolio.” He also advocates for continuous testing. “Small changes — even things like how you present an offer — can have measurable results. The key is to iterate constantly and use what works.”
Overcoming Organizational Barriers to Data Integration
Even with robust analytics tools, many institutions stumble when trying to embed data-driven decisioning into their credit strategy. The most common mistake is launching initiatives only to abandon them before results take hold. Smaller banks may lack the analytical depth of larger institutions, making external partners valuable allies for insight and execution. “Whatever you do, don’t treat analytics as a one-time project,” Telzrow says. “It has to be a continuous, evolving process.”
Technology investment, data capacity, and culture are also critical considerations. Consumer lending has long embraced data, but commercial lending has historically relied on relationships and handshake deals. “Culturally, commercial banking still values personal familiarity,” Telzrow says. “But that can’t be the only basis for decisions anymore. Competition is moving too fast.”
Today’s lenders must adapt to the pace of alternative lenders and digital-first platforms. “You can’t compete with someone who can issue 20 loan offers in 30 minutes unless you’re equally efficient,” Telzrow says. “That means breaking down cultural barriers, modernizing processes, and trusting the data.”
Navigating AI and the Future of Credit Analytics
Artificial intelligence has long played a role in credit analytics, with credit agencies and scoring models using machine learning for years. “What’s changed is accessibility,” Telzrow notes. “Tools like ChatGPT and Copilot have brought it into the mainstream.”
The real potential of AI lies in helping organizations stay alert to emerging risks and threats. “AI can be used to misrepresent identities — individuals or businesses,” he warns. “When you’re lending significant sums, that kind of fraud is a serious concern.” Looking ahead, Telzrow predicts a future that may resemble a “battle of AIs,” where institutions deploy their own intelligent systems to detect and prevent AI-generated fraud.
“It’s a constant evolution,” he says. “Lenders must stay vigilant while using AI to enhance decisioning and customer experience.”
The Leadership Imperative: Modernize and Adapt Quickly
His single best piece of advice to leaders? “Fail fast.” Progress depends on the willingness to experiment, learn quickly, and pivot when needed. Leaders need to regularly revisit foundational loan policies that guide credit decisioning, ensuring they reflect current realities rather than outdated assumptions.
“Many treat loan policy as a third rail — something you don’t touch,” he says, “but it has to evolve. The goals you set three years ago, even three months ago, might no longer align with your market,” he says, encouraging institutions to continually assess what’s working and what’s not, then adjust course with speed and confidence.
That adaptability is what defines forward-thinking lenders. “Credit decisioning is like fire — it can cook your meal or burn your house down. Managing it effectively means using data to generate growth while keeping risk contained.”
To connect with Jonathan Telzrow and learn more about his work, visit his LinkedIn.
