Can Ensemblex Build a Good Credit Model in My Market?

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 still looking for lenders in Antarctica), but chances are you know your market better than we do. In fact, even in countries where we have lots of experience, we never assume we know a client’s specific market better than the client does.

Instead, our approach is built on collaboration. We build models with clients, not for clients. Here’s a bit more on how the process works.

Credit Fundamentals Travel Well

Some things are universal. For example:

• Modeling practices like data sampling, algorithm selection, and performance metrics

• Best practices in data hygiene, feature engineering, and explainability

• Model monitoring frameworks to ensure long-term performance and stability

Whether we’re working with U.S. credit bureau data or open banking data from Latin America, we apply the same core process: define the outcome, assess data quality, tailor the feature set, and build models that optimize for the actual business goal.

Where We Rely on You

Here’s where we expect to learn from you:

1. Business context

We start every engagement by understanding how your business operates, what your model needs to predict, and how you’ll use it in practice.

That includes:

• Your top-level goals, so that we can determine together what outcome to model (e.g., first payment default, charge-off, churn)

• How you define risk in your market

• Where your model will sit in your credit strategy or product funnel

That early understanding shapes everything from how we engineer features to how we evaluate performance.

2. Local data

In the U.S., credit bureau data is robust, consistent, and built for modeling. In many other markets, it’s not.

We’ve worked with:

• Sparse or inconsistent bureau data

• Open banking or bank transaction data

• Custom internal systems and non-traditional data sources

We don’t assume we know your data on day one. Instead, we take an active role—examining its structure, cleaning inconsistencies, and learning its quirks. We do data diligence: How is this data collected? How stable is it? Is it influenced by government policy or market volatility?

3. Competitive dynamics

You know:

• What products your competitors offer

• What pricing or approval gaps you want to exploit

• Where you need to differentiate

We take that input and help build a model that reinforces your advantage.

4. Macro and regulatory environment

We’ve worked in markets where:

• Regulators are drafting AI guidance in real time

• Credit reporting laws change abruptly

• Socioeconomic factors shift dramatically over short periods

Our model development practices—like SHAP-based explainability, strong variable selection discipline, and ongoing monitoring—create a model robust to your specific environment.

The Combination: A Collaborative Process

All together, here’s what the process looks like.

1. Intensive upfront work

We spend 2–6 weeks analyzing your data, talking with your team, and learning your business before building anything.

2. Custom development

We build every model from scratch. Nothing off-the-shelf.

3. Clear checkpoints

We check in at each stage and validate data definitions, business logic, and performance expectations with your team.

4. Joint evaluation of results

We go beyond AUCs. We look at predicted vs actual bad rates by tier, conversion impacts, and how the model aligns with your underwriting policies.

5. Ongoing support and monitoring

We stay in close contact post-launch, tracking feature and score stability, flagging issues, and adapting to changes.

We’ve used this approach to deliver successful models across a range of verticals and markets, from auto lenders in the U.S. to digital lenders in Central America and banks in the EU. If you’re building a credit model in a market you think we haven’t seen—don’t let that stop you from reaching out. We’re confident in what we bring to the table. And we’re even more confident in the results we can achieve together.