It’s a question we get from nearly every client with a credit model in production. Once you’ve launched a model, how often should you revisit it? Is there a fixed schedule you should follow? Or is it only necessary when something breaks? As with many modeling questions, the answer is: it depends. There are some clear principles we use to guide retraining cadence — and some concrete signs that it’s time to act. What we mean by retraining Let’s start by clarifying what we mean

“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

We get the question almost every week—from Series A founders, Heads of Lending at established fintechs, even board members. They’re usually questioning if they can wait: wait until they’ve raised more capital, collected more data, or even wait to see if they can stem a rising risk crisis themselves. An Executive Risk Advisor (ERA) is an investment, and our clients want to make it judiciously. Well, the answer is straightforward. We’ll get into common hesitations and our advic

One of the most common questions we hear about credit model modernization is this: “We use a lot of underwriting rules—how can we safely reduce or replace them?” Or more bluntly: “Our model isn’t doing much. Most of the real work is happening in the knockouts.” Many lenders start with a simple rules-based credit policy, planning to grow out of it over time. But shedding the knockouts is easier said than done. What starts as a short-term solution often turns into a long-term l

In short: Yes, we can. But not without you. Potential clients, especially in emerging markets, often ask us how well we understand the peculiarities of their market. It’s a good question. A successful model is built for the context in which it will operate: the unique customer dynamics, regulatory constraints, and data ecosystems. Otherwise, while it might look attractive in testing, it’s likely to cause headaches in production. We’ve now worked on most continents (we’re stil

"If it ain't broke, don't fix it." Lenders often push back when we suggest exploring a new model build. It's fair—model builds require resources, and it can feel silly to fiddle with an underwriting model that "works," especially if origination volumes are on track and losses seem manageable. But at Ensemblex, we know that "works" often means "leaves money on the table." What Does It Mean for a Model to "Work"? In technical terms, a credit model is effective if it "slopes ris

What Exactly Is a Leaky Variable? A leaky variable is any feature in your training data that contains information you won't have at decision time. The most extreme example would be using default status to predict default. That creates a perfect model in development with zero real-world utility, as the model will simply learn to predict the outcome with itself. Of course, any serious data scientist would catch an error that massive. But leakage can be subtle: Post-application

Gradient Boost Models (GBMs) have become the go-to tool for many credit modelers for good reason. GBMs can unlock meaningful lift in predictive accuracy, helping lenders better distinguish between high- and low-risk applicants, expand safe approvals, and reduce losses. But with great power comes great risk. At Ensemblex, we’ve spent years developing, testing, and monitoring GBM credit models. And while we remain strong advocates for their use in the right context, we also kno

FICO and Vantage scores are well-known, widely used, and easily accessible. Meanwhile, building a custom credit model can take significant resources. So why build your own? Let's dive into how FICO and Vantage work, how custom models differ, and why custom models can deliver meaningful performance gains. What Are FICO and Vantage? FICO and Vantage scores are generic, off-the-shelf credit scores built using credit bureau data from TransUnion, Experian, and Equifax. FICO has be

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 histor