How Much Data Do I Need to Build a Credit Model?

One of the most common questions we get about building a credit model is "How much data do I need to make it work?"

And like many questions in credit modeling, the answer is "it depends." In this post, we’ll break down the data volume question across three dimensions: rows, columns, and outcomes, and how those impact your modeling approach.

Row Count: How Many Records Do You Need?

Most clients asking about data volume are thinking of the row count (the number of historical applications or loans) they need to build a model. The short answer: if you have fewer than a few hundred observations, you’re probably not ready for a model yet. But if you’ve crossed into the thousands, or better yet, the tens or hundreds of thousands, you’re in a much stronger position.

At Ensemblex, we’ve built effective machine learning models with as few as 3,000–4,000 rows, especially when default rates are high and outcomes are well-labeled. But as a rule of thumb, machine learning models begin to shine when you reach tens or hundreds of thousands of rows.

The value of more data doesn’t stop, either. Unlike traditional scorecards, machine learning models continue to extract value from additional data, even into the millions of records. The marginal benefit may decline—adding 100K rows to a dataset of 2M won’t give the same boost as adding 100K to a set of 10K—but we’ve never seen the gains completely plateau.

That said, you don’t always need to use every row. In one project with over 5 million records, we found that accuracy gains tapered off around 2 million records. We stopped there. Not because the data wasn’t valuable, but because the computing costs outweighed the business benefit of more modeling precision.

Column Count: How Many Predictors Should You Have?

Beyond rows, you need predictors (features or “columns” that describe the applicant or loan). Traditional logistic regression models often use a few dozen columns. Ensemblex’s machine learning models typically use 50–200 features, depending on data richness and modeling goals. You’ll want to have more than the data from your application form. The best-performing models are built on diverse, complementary data sources:

• Credit bureau data

• Alternative bureau data (e.g., payday loans, checking history)

• Cash flow or bank transaction data

• Application and behavioral fields

• Internal servicing data

We often find that mixing data sources unlocks more lift than trying to squeeze more juice from a single one. Fortunately, many third-party providers offer datasets with thousands of potential variables—many redundant or correlated, but still rich with signal if used wisely.

Outcomes: The Most Important Column

You can have thousands of records and columns, but if you don’t have a clear outcome variable, you don’t have a model.

In credit, your target might be default, charge-off, serious delinquency, or payment behavior. But critically, you must have those outcomes for the records in your training set. If you’ve only collected applications and haven’t yet observed performance, you can’t train a credit model.

Outcomes also narrow down your usable row count. For example, if your model predicts 90-day delinquency within 12 months, you’ll need loans at least 12 months old. So even if your database has 200,000 recent loans, your effective modeling set might be just a fraction of that.

However, if you don’t have enough rows with outcomes in your own business data, there are tricks to build a dataset with outcomes from a third party like a credit bureau. (We’ll save a detailed discussion of this for a later post…)

Outcome Window: How Long Should You Wait?

Another subtle but important piece: over what period do you observe performance? There’s a trade-off. Shorter windows (e.g, 30+ days delinquent in the first 3 months) allow for faster iteration and bigger modeling sets. Longer windows (e.g, charge-off by month 24) give more predictive targets but reduce sample size and increase lag.

At Ensemblex, we often test multiple outcome windows simultaneously—from early delinquency to late-stage default—and assess them against common holdout sets. This helps us balance statistical power, business relevance, and iteration speed.

And don’t forget seasonality: if your business changes month to month, you’ll want at least 12–24 months of vintage coverage to train a model that accounts for cyclical behavior.

Bonus: What Kind of Model Are You Building?

How much data you need also depends on what kind of model you’re building. If you're creating rules-based systems or decision trees, a smaller dataset may suffice. For logistic regression, you want more data and stronger outcomes. And for machine learning, which is the gold standard for modern credit modeling, you need meaningful volume across rows and columns, with an ample number of outcomes, to see real benefit. That said, machine learning can still perform well at lower data volumes and can grow with your business. As you collect more records, the model can improve without a full redesign.

Final Thoughts

So, how much data do you need?

• Fewer than 1,000 records: Probably not worth modeling yet.

• 3,000–10,000 records: Logistic regression or simple models will work. (Though leaning into machine learning sets you up for future success.)

• 10,000–100,000+ records: You’re ready for machine learning.

• Millions of records: Congratulations—just watch for diminishing returns.

In all cases, what matters most is data quality, outcome availability, and model purpose. If you’re navigating this process, we’d love to help. Ensemblex has helped lenders at every stage—from pre-launch startups to multi-billion-dollar portfolios—get the most from their data.

Have questions about your own dataset? Drop us a line. We’re always happy to talk data.