The digital revolution has transformed lending, and at the heart of this transformation stands Daryl Sandoval, a finance leader and Certified Credit Analyst whose career spans 25 years in rural banking and microfinance. While artificial intelligence promises to streamline credit decisions—from predictive analytics to automated underwriting—Sandoval argues that its true power lies not just in efficiency but in how it complements human judgment.
At Bank of Makati’s Micro and Small Lending Division, where he oversees loans to farmers, small shop owners, and seasonal workers, Sandoval has mastered the delicate balance between cutting-edge technology and the nuanced realities that define risk in sectors where stability often depends less on financial metrics than on local context.
Consider the case of agri-machinery financing—a critical but high-risk category for rural borrowers. An AI-driven algorithm might flag a farmer’s credit score as strong, yet it can’t account for the unpredictable fluctuations of harvest cycles or supply chain disruptions that could derail repayment.
Sandoval explains, “Risk isn’t just numbers—it’s about understanding businesses, communities, and the impact of responsible lending.” His approach begins with data: AI tools help identify potential defaults early by analyzing patterns in late payments or cash flow trends. But before finalizing any decision, he and his team dive deeper into the human side of the equation. “We use AI for its predictive power, but we cross-check everything with field visits,” Sandoval says. “A farmer’s loan approval shouldn’t hinge on a spreadsheet alone—it should be validated by someone who knows their village.”
The result is a hybrid system that leverages technology without sacrificing human insight. For instance, when Bank of Makati introduced a simplified approval process for PUV loans—a critical lifeline for many rural drivers—the team first used AI to identify high-risk applicants based on repayment history and financial health.
However, they didn’t stop there. “We paired that with direct engagement,” Sandoval recalls. “We visited the borrowers’ shops or factories, talked to their neighbors, and ensured the loan terms were realistic—not just mathematically sound.” The outcome? A 40% reduction in approval times while maintaining repayment rates that remained strong, proving that even in an era of digital banking, empathy remains a cornerstone of responsible lending.
Yet Sandoval isn’t blind to the challenges posed by AI’s rapid advancement. He warns that over-reliance on algorithms can lead to unintended consequences, particularly in sectors where local knowledge is indispensable. “If an AI model doesn’t account for local realities—like a village’s reliance on seasonal labor or unpredictable harvests—it can create unsustainable debt,” he cautions.
This is why his team emphasizes partnerships with local agents and cooperatives to ensure loans reach underserved communities. At the same time, Sandoval advocates for a future where AI serves as an extension of the analyst’s role rather than a replacement. “AI can’t replace empathy—but it can amplify what we already do best,” he says. For example, when AI flags potential fraud—such as multiple large withdrawals from a borrower’s account—a human analyst steps in to verify the story, ensuring that no one is unfairly penalized for oversights in the system.
The future of credit analysis, according to Sandoval, lies in collaboration rather than competition between humans and machines. He envisions a world where AI handles routine tasks—like stress-testing loan portfolios or identifying high-risk patterns—and analysts focus on what they do best: designing innovative products, resolving disputes, and mentoring teams.
“We’re testing AI-powered chatbots for micro-loans,” Sandoval explains, “where borrowers can ask questions like ‘Will my loan be approved if I lose my job?’ The bots provide data-driven answers, but a human analyst follows up to ensure the borrower understands risks.” This approach ensures that technology serves the needs of both lenders and borrowers, creating a more inclusive and transparent lending ecosystem.
For finance leaders navigating this new landscape, Sandoval’s story offers a roadmap. The key isn’t to reject AI outright but to recognize its potential as a tool—one that enhances rather than replaces human judgment. As the industry continues to evolve, the best risk assessments will be those that balance data with empathy, scalability with local knowledge, and innovation with responsibility. In an era where every loan decision can impact millions of lives, the ability to strike this delicate balance isn’t just a competitive advantage—it’s essential.
