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“How many features should I have in my model?” It sounds like a simple question. But like so many modeling decisions—especially in the credit space—the honest answer is: it depends . We often hear this question from internal teams and executives alike. It comes up when a team is building its first in-house model, when it's looking to upgrade an existing scorecard, or even when trying to explain why their current model looks the way it does. At Ensemblex, we've built models fo

A founder’s guide to the paths, tradeoffs, and decision points that matter There’s more than one way to launch a credit card. If you’re a fintech founder or early product lead exploring how to offer a credit product, you’ve likely discovered this already: there’s no single blueprint. Just a handful of viable paths each with their own infrastructure choices, regulatory implications, cost profiles, and long-term consequences. This post breaks down the core approaches, the real-

Lenders know their own portfolios inside and out. But when it comes to growth, risk management, and product strategy, the smartest players don’t just look inward — they look outward. “Off-us” data — information about customer behavior and lending performance outside a company’s own book — is becoming a critical lever for success. Here are a few ways we’ve seen lenders use off-us data to sharpen decisions: Benchmarking Performance One lender in Asia used bureau data to compare

A tactical guide for fintech founders planning to fund credit at scale You’ve validated the product. You’re ready to scale. Now comes the big question: where’s the capital coming from? For fintech teams launching credit card products, getting a debt facility in place is one of the most important—and misunderstood—steps in building a durable lending business. If you’re planning to originate credit at scale, you’ll eventually need outside capital to do it. But not all debt is c

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

For many financial services companies, lending is the next big step. Payments, deposits, and other services create a strong base, but credit creates a large, profitable business. The challenge? Well, it’s credit. Not just another product feature, a mismanaged credit product can sink a business. One solution is to move very slowly, but that has its own costs: wasted runway, time, and opportunity. At Ensemblex, we know that credit and fast growth are compatible under discipline

If you already have smart people on your team—and you’re not looking to fully outsource your modeling work—why bring in an outside partner? We’ve partnered with all sorts of modeling teams, from small startups creating their first model to established lenders and banks with mature modeling talent and fully built-out systems. Across all of them, we’ve learned how to make our presence a multiplier, not a crutch. Here’s how. 1. We meet you where you are We don’t sell prebuilt mo

What operations infrastructure do you need beyond your LMS and LOS? Even “full-suite” LMSs and LOSs will leave some gaps. Adding to the...

We get the question almost every week—from Series A founders, Heads of Lending at established fintechs, even board members. They’re...