Are You Approving the Right Borrowers—or Risking the Wrong Ones?

In Q1 of 2023 alone, the four largest U.S. lenders wrote off $3.4 billion in bad consumer loans. That level of risk is avoidable—with better data.

Relying only on what applicants provide or what’s in your internal systems isn’t enough. Without third-party validation, financial institutions risk lending to the wrong people—and missing out on deserving borrowers with thin credit files.

🧠 External data helps institutions assess creditworthiness with greater confidence, accuracy, and reach.

Why External Data Sharpens Credit Decisions

With third-party data layered into your credit evaluation process, you can:

✅ Validate applicant income, employment, and debt obligations in real time
✅ Expand access to credit for applicants with limited history
✅ Reduce loan defaults by spotting early red flags
✅ Build more accurate credit risk scores using verified external data
✅ Improve underwriting decisions while boosting revenue and inclusivity

How It Works

🔹 Combine applicant-provided info with verified external data like payment history, income, or utility bills
🔹 Train machine learning models to flag risky applications or identify high-potential borrowers with limited credit history
🔹 Incorporate real-time economic and market data to adjust lending criteria dynamically
🔹 Automate data checks across public records, rental payments, and third-party credit reports

Real-World Impact: Broader Credit Access with Less Risk

Most major lenders use external credit bureaus to verify credit scores and histories—but the next frontier is expanding that scope. By using alternative third-party data, financial institutions can serve more people while lowering default rates.

📩 Want to make smarter lending decisions with better data?

Let’s talk about how Blue Street Data can connect you to the most relevant third-party sources to upgrade your creditworthiness assessments.

👉 Talk to a Data Expert

Live Webinar

Is “Quality” Killing Your AI? Defining Data Fit for Strategic Success

February 18th, 2026 / 1:00 PM EST

Every data investment carries risk unless you know how to measure its “fit” for the mission. Many organizations assume that “high-quality” data is sufficient for AI and analytics, only to discover too late that data fit is the real determinant of success. In this live session from Blue Street Data’s Building with Better Data series, Andy Hannah and Malcolm Hawker unpack why data that works for BI can be dangerous for AI, leading to model failure, wasted spend, and lost trust. You’ll learn how to define, measure, and validate data fit so your models deliver reliable, business-aligned outcomes. Reserve your spot today!