In short: Lenders choosing how to run their credit risk model generally have three paths: build it in-house, buy an off-the-shelf score, or partner with a provider for a model built around their own loan book. Each path trades off differently on cost, control, and how well the model actually fits the lender's business. The right choice depends on team size, timeline, and how differentiated the lender's book really is.
Lending teams are under more pressure than they used to be. Boards want stronger risk-adjusted growth, not just growth. Competitors, including newer fintech lenders, are making faster and more consistent decisions. And the data available to lenders, from transaction history to repayment behaviour, has grown well beyond what a traditional credit score alone can capture. Bridging the credit risk analytics gap is critical to turn this data into actionable insights.
Against that backdrop, the question of how to run a credit risk model isn't a one-time technology decision anymore. It's closer to a strategic one, because the model a lender uses shapes who gets approved, how quickly, and how consistently across the whole portfolio.
Most lenders land on one of three paths. It's worth being honest about what each one actually involves before picking one.
Building in-house means hiring or assigning a team to develop, test, and maintain a model using the lender's own data. It gives full control over the model and the process.
The trade-off is time and ongoing ownership. A model isn't a one-off deliverable, it needs to be maintained as the lending book changes, as new products launch, and as the team's understanding of risk evolves. That ongoing commitment is often underestimated at the point of deciding to build, and it's worth its own closer look (more on that below).
Buying an off-the-shelf score is the fastest path to something working. It's a known, proven product, and it doesn't require an internal build.
The limitation is that a generic score is built on a generic population, not this lender's actual borrowers. A model that wasn't built around a lender's own book will always be making its best guess about that lender's risk, rather than reflecting it directly. For lenders with a distinctive customer base, product mix, or risk appetite, that gap can show up as approvals that don't quite match the lender's actual risk tolerance, in either direction. When you compare credit risk models, these limitations become apparent.
The third path sits between the other two. Rather than building from scratch or buying something generic, a lender partners with a provider to get an explainable lender-specific model built around their own loan book, covering underwriting, offer setting, servicing, and portfolio monitoring, without replacing their existing systems or handing over decision control.
This is sometimes called a model-as-a-service arrangement. In plain terms, it means the lender gets a model shaped by their own data and risk appetite, supported on an ongoing basis, without needing to build and maintain the underlying infrastructure themselves
A few questions tend to make the decision clearer:
There's no universally right answer here. A large lender with a dedicated risk team and a highly distinctive book might reasonably choose to build. A lender that just needs a quick, standard product might reasonably buy. But for lenders who want a model that reflects their specific business without carrying the full weight of building and maintaining it themselves, partnering is often the path that best balances those trade-offs.
Consider a mid-sized consumer lender with a small in-house risk function and a growing, somewhat non-standard borrower base.
Building in-house would mean pulling their risk lead off other priorities for months, with an uncertain end date. Buying an off-the-shelf score would get them moving quickly, but it wouldn't reflect the parts of their borrower base that make them different from a typical lender. Partnering for a lender-specific model gives them a model built around their actual book, without needing to grow their risk team to support it. For a team in that position, the partner path tends to offer the closest fit between speed, control, and long-term fit.
Carrington Labs works with lenders who want the third path: explainable credit risk models built around their own loan book, covering underwriting, offer setting, servicing, and portfolio monitoring, without replacing existing systems or handing over decision control. Our models are informed by real-world lending experience, including Beforepay's own lending scale, though results vary by lender and loan book.
If you're weighing up these options for your own lending business, we're happy to talk through where a lender-specific model might fit, and where it might not.
