Category: Validation & Testing

Champion-Challenger Testing

Also known as: Champion/Challenger Testing, Champion Challenger Model, Champion/Challenger Test

Simply put

Champion-challenger testing is a method for comparing a model or strategy currently in use (the 'champion') against one or more alternative candidates (the 'challengers') to see which performs better. The comparison is typically done using live or real-world data before deciding whether to replace the current model. It is a way to test potential improvements without immediately switching away from the existing production model.

Formal definition

Champion-challenger testing is a comparative evaluation approach in which the live performance of an incumbent production model (the champion) is measured against one or more candidate models (the challengers), commonly using production or live data, to inform whether a challenger should be promoted to production. In model risk management contexts it is often characterized as a validation-adjacent method for assessing candidate models against alternatives before deployment, though it is also applied more broadly to competing strategies in decision management, marketing, and workforce management. As presented in the available evidence, definitions vary by domain and no single authoritative specification of the methodology is established; the technique supports ongoing model comparison and selection but does not by itself constitute a complete validation program, and its scope, statistical design, and controls should be defined relative to the organization's governance and risk frameworks.

Why it matters

Champion-challenger testing matters because it allows organizations to evaluate whether a candidate model or strategy improves on the one currently in production without prematurely abandoning a known, functioning incumbent. This creates a controlled path for continuous improvement: the existing champion continues to serve production decisions while challengers are measured against it, reducing the operational and risk exposure that can accompany an abrupt model switch. In model risk management contexts, this ongoing comparison supports informed promotion decisions and can feed into broader monitoring of whether a deployed model still performs adequately.

The technique is relevant across several domains, and its meaning shifts accordingly. In risk model validation settings it is sometimes characterized as a validation-adjacent method for testing production models against alternatives on live data before any change is made; in decision management, marketing, and workforce management, it is applied more generally to competing business strategies. Because definitions vary by domain and no single authoritative specification of the methodology is established, professionals should be careful to scope the term to their own context rather than assuming a uniform standard.

A common pitfall is treating champion-challenger testing as equivalent to a complete model validation program. It is not. The technique supports model comparison and selection, but it does not by itself establish that a model is conceptually sound, well-implemented, or fit for its intended use. Its statistical design, data controls, and governance placement should be defined relative to the organization's own risk framework, and it should be understood as one component within a broader validation and monitoring approach rather than a substitute for one.

Who it's relevant to

Model risk managers and validators

For those responsible for model risk, champion-challenger testing offers a way to compare production models against candidate alternatives on live data before a change is made. It is best treated as a validation-adjacent method that supports model comparison and selection, not as a stand-alone validation program; its design and controls should be defined within the organization's risk framework.

Data scientists and model developers

Developers use the approach to test whether a new candidate model improves on the incumbent using real-world performance rather than only offline evaluation, providing a controlled path to promote improvements while the existing model continues to serve production.

Decision management and strategy teams

In decision management, the technique is applied to competing business strategies to improve their success. Here the 'champion' and 'challenger' may be strategies rather than statistical models, and the term should be scoped to that domain rather than assumed to match a risk-validation meaning.

Marketing, sales, and workforce management practitioners

These teams apply champion-challenger testing as a systematic way to compare two or more competing strategies or approaches to determine which performs better. As with other domains, the definition and rigor of the method vary by context and should be defined accordingly.

AI governance and compliance professionals

Governance and compliance staff should understand where champion-challenger testing sits within model oversight—supporting ongoing comparison and informed promotion decisions—while recognizing it does not by itself constitute a complete validation program or eliminate model risk. Its governance placement and controls should be specified explicitly.

Inside Champion-Challenger Testing

Champion Model

The incumbent model currently deployed in production and used for live decisioning. It serves as the baseline against which alternative models are compared.

Challenger Model

One or more candidate models run alongside or in parallel with the champion to test whether they produce improved or comparable outcomes. Challengers may differ in methodology, features, or specification.

Comparison Metrics

The predefined performance and outcome measures used to evaluate champion versus challenger, which may include discrimination, calibration, stability, and business-relevant indicators. The specific metrics chosen depend on the model's use and context.

Evaluation Design

The structure governing how challengers are exposed to data or decisions, such as parallel scoring on the same population (shadow deployment) or, in some setups, controlled allocation of live traffic. The design determines what conclusions can be drawn.

Promotion and Governance Criteria

The documented thresholds and approval steps that determine whether a challenger replaces the champion. In governed environments this typically involves oversight, sign-off, and change-control processes rather than automatic substitution.

Monitoring Linkage

The connection between champion-challenger testing and ongoing model monitoring, since testing is frequently used both at selection and as a continuous mechanism to detect when a deployed champion may warrant replacement.

Common questions

Answers to the questions practitioners most commonly ask about Champion-Challenger Testing.

Is champion-challenger testing the same as model validation?

No. Champion-challenger testing compares the performance of an incumbent model (the champion) against one or more alternative models (challengers) to inform whether a replacement is warranted. It is typically an ongoing performance-comparison and model-selection practice. Model validation, as commonly framed in model risk management guidance such as SR 11-7, is a broader independent assessment of conceptual soundness, data, implementation, and ongoing monitoring. A challenger model would itself typically require validation before promotion; champion-challenger testing does not substitute for that validation.

Does a challenger outperforming the champion mean the challenger should automatically be deployed?

Not necessarily. Superior performance on a chosen metric during testing does not by itself justify promotion. In many frameworks, a decision to replace a champion also considers factors such as stability over time, the appropriateness of the comparison metric, potential overfitting to the test period, governance sign-off, and independent validation of the challenger. Outperformance is an input to the decision, not the decision itself, and performance improvement should be distinguished from reduced model risk.

How is a challenger model typically promoted to champion?

Promotion pathways vary by organization, but they commonly involve predefined criteria, a review or governance approval step, and, in many model risk management contexts, prior independent validation of the challenger. Some organizations require the challenger to demonstrate advantage over a sustained observation window rather than a single period. The specific thresholds and approval structures are organization- and sector-specific, so this should be documented in internal policy.

What metrics are used to compare champion and challenger models?

The comparison metric should align with the model's intended use and the associated risk. Selection of an inappropriate or single metric is a frequent pitfall. Organizations often evaluate multiple dimensions—predictive performance, calibration, stability, and, where relevant, fairness-related measures—rather than relying on one aggregate score. The appropriate metrics are context-dependent and should be defined before testing begins.

Can champion and challenger models run in parallel on live traffic?

In some implementations challengers are run in a shadow or parallel mode, generating outputs that are recorded but not acted upon, so their behavior can be observed against real data without affecting decisions. Other implementations rely on offline or holdout comparisons. The chosen approach carries different operational and governance considerations, and any parallel running should respect applicable controls over data use and decision-making.

How does champion-challenger testing relate to ongoing monitoring?

Champion-challenger testing can support ongoing monitoring by providing a benchmark against which the incumbent's continued suitability is assessed, which may help surface performance degradation. It does not, however, replace monitoring of the champion itself. The two are complementary: monitoring tracks whether the deployed model still performs as expected, while challenger testing explores whether an alternative would perform better under defined criteria.

Common misconceptions

A challenger that outperforms the champion on a chosen metric should be promoted automatically.

Outperformance on one or more metrics is typically one input into a governed decision, not the decision itself. In many risk-management settings, promotion requires validation review, assessment of stability and residual risk, and appropriate approvals before a challenger replaces a champion.

Champion-challenger testing is a form of model validation.

The two are distinct. Champion-challenger testing is a comparative and often ongoing performance mechanism, whereas validation is a broader independent assessment of whether a model is conceptually sound and fit for its intended use. Testing may inform validation but does not substitute for it.

Because it compares models continuously, champion-challenger testing eliminates model risk.

It is a control that can help detect performance degradation and surface better-performing alternatives, thereby reducing and managing certain risks. It does not remove model risk, and both champion and challenger models remain subject to their own limitations and monitoring needs.

Best practices

Define comparison metrics and promotion thresholds in advance, tied to the model's intended use, so decisions are not driven by post hoc selection of favorable metrics.

Keep champion-challenger testing distinct from, and complementary to, independent model validation, and route any proposed champion replacement through documented change-control and approval processes.

Ensure champion and challenger are evaluated on comparable data or populations so that observed differences reflect model behavior rather than differences in inputs or exposure.

Assess challengers on stability, calibration, and other risk-relevant dimensions, not solely on a single discrimination or accuracy metric.

Maintain documentation of the testing design, results, and rationale for promotion or rejection to support governance, oversight, and auditability.

Treat champion-challenger testing as an ongoing monitoring input, using it to flag potential degradation of the deployed champion while recognizing that any replacement still requires appropriate review.