Most lenders we work with already have a sizable pile of data—application fields, bank data, bureaus, device signals, platform behavior. When building a credit model, not all data is good data. Some data actually carries long-term risk not just to your model, but to your entire business. Here’s a quick guide to evaluating your data. Here’s a snapshot of what we often see on our first call with a lender: Application data : Self-reported income, employer name, product type, pur

Every credit card application flow will include some sort of bureau data request, often called a credit "pull". There are multiple kinds of credit pulls: "hard" and "soft". What type you use and when may seem like a subtle difference, but, as is a theme in lending, nuances can have outsized impacts. How and when you pull credit will impact your conversion, customer experience, and risk management. First, at a technical level: Soft pulls give you access to a consumer’s credit

The Short Answer: Yes, and Don't Cut Corners When investors, debt providers, and sponsor banks ask to see your credit policy, it may seem like just another box to check. Can’t we just explain the model and include some example flows? Can I whip one up with some help from ChatGPT and a few templates? But while it might seem like a boring document, the credit policy is a terrible place to skimp. Those dozens of pages prove that you're a serious lending business ready to lend re

Why Sponsor Banks Matter So Much Choosing a sponsor bank isn't just another box to check. It’s a consequential decision that influences your compliance obligations, user experience, and product velocity. The right sponsor can save you months of delay (and a lot of rework). But it’s not always obvious what you should be looking for—or what they’re looking for from you. Let’s break it down. What You Should Look for in a Sponsor Bank When evaluating the compatibility of a pote