Category: Validation & Testing

Challenger Model

Also known as: Challenger, Challenger Model Framework

Simply put

A challenger model is an alternative model developed to compete against and test an organization's currently deployed model, often called the 'champion' model. By comparing how the challenger performs relative to the champion, an organization can assess whether its existing model is still the best available choice. Note that the term 'Challenger Model' is unrelated to the 'Challenger Sales' methodology, which is a separate sales technique that happens to share a name.

Formal definition

In the context of model risk management, a challenger model is a candidate or alternative model evaluated against an incumbent 'champion' model as part of a structured champion-challenger framework used to assess and benchmark model performance. Based on the evidence available, such frameworks are commonly applied in finance and risk management to evaluate the performance of an existing (champion) model against one or more alternatives. The evidence provided does not specify the precise evaluation criteria, statistical tests, or promotion/replacement procedures used, and these typically vary by institution and regulatory context; readers should note that this term is distinct from model validation and from the unrelated 'Challenger Sales' methodology.

Why it matters

The champion-challenger approach matters because a deployed model's suitability is not static. Business conditions, data distributions, and available modeling techniques change over time, and a model that was the best available choice at deployment may no longer be. Maintaining one or more challenger models gives an organization a structured way to test whether the incumbent (champion) still represents the best option, rather than assuming continued adequacy by default.

Within model risk management, challenger models support ongoing benchmarking and can inform decisions about whether to retain, retrain, or replace a production model. This is distinct from model validation, which independently assesses whether a model is conceptually sound and fit for purpose; benchmarking against a challenger is one input that can support such activities but does not substitute for them. The distinction is worth preserving because professionals sometimes treat the existence of a challenger as evidence of validation, when the two serve different governance functions.

A practical caution: the term 'Challenger Model' is frequently confused with the unrelated 'Challenger Sales' methodology, a sales technique built around teaching, tailoring, and taking control of the buying process. Despite the shared name, the two concepts have no relationship, and conflating them can introduce confusion in cross-functional discussions. The evidence available does not specify the precise evaluation criteria, statistical tests, or promotion and replacement procedures used in champion-challenger frameworks; these typically vary by institution and regulatory context.

Who it's relevant to

Model risk managers

Those responsible for identifying, measuring, and monitoring model risk may use champion-challenger frameworks as one structured method for benchmarking a deployed model against alternatives. They should treat challenger comparison as distinct from independent model validation rather than as a replacement for it.

Data scientists and model developers

Practitioners who build and maintain models may develop challenger models to test whether newer techniques or retrained models outperform the incumbent. The specific evaluation criteria and any promotion procedures typically depend on institutional policy and are not standardized across the field.

Financial and risk professionals

The evidence indicates that champion-challenger frameworks are commonly applied in finance and risk management to evaluate an existing model's performance against alternatives. Professionals in these settings use the approach as part of ongoing performance benchmarking.

Auditors and reviewers

Those reviewing model governance may examine how challenger models are used and how comparisons feed into retention, retraining, or replacement decisions. They should be aware that the presence of a challenger does not by itself demonstrate that a model has been validated or that its risk has been controlled.

Inside Challenger Model

Benchmarking function

A challenger model is an alternative model developed to compare against the model currently in production (often called the champion). Its primary role is to provide a point of comparison for performance, stability, and risk characteristics, rather than to serve as the deployed decision-making model.

Independent or alternative specification

Challenger models typically use a different methodology, algorithm, variable set, or set of assumptions from the champion. This contrast is what makes the comparison informative; a challenger that merely replicates the champion offers limited value for identifying weaknesses.

Role in ongoing monitoring

In many model risk management practices, challenger models support the ongoing monitoring component by helping detect whether the champion's performance is deteriorating relative to plausible alternatives. This relates to, but is distinct from, formal validation.

Governance and documentation context

The use, scope, and limitations of a challenger model are typically documented so that reviewers understand what the comparison does and does not demonstrate. This documentation sits within broader model risk management and AI governance structures without being a substitute for either.

Decision inputs, not automatic replacement

Challenger results generally inform judgment about whether to investigate, recalibrate, revalidate, or eventually replace a champion. The promotion of a challenger to production is normally a governed decision rather than an automatic outcome of outperforming the champion.

Common questions

Answers to the questions practitioners most commonly ask about Challenger Model.

Does a challenger model replace the champion (production) model once it performs better?

Not automatically. A challenger model is typically used to test, benchmark, or stress the incumbent "champion" model, but outperforming the champion on one or more metrics does not by itself trigger replacement in most governance frameworks. Promotion decisions generally require validation, documentation, and sign-off through established change-management and model risk controls. Treating the challenger as an automatic successor conflates a benchmarking tool with a formal deployment decision.

Is running a challenger model the same as validating the champion model?

No. Challenger models can inform validation by providing a comparative benchmark, but they are not a substitute for it. Validation, as commonly framed, is a broader independent assessment of a model's conceptual soundness, data, implementation, and ongoing performance. Using a challenger as a benchmark is one possible input to that process, not the process itself, and relying on a challenger alone leaves key validation elements unaddressed.

How is a challenger model typically used in ongoing monitoring?

In many frameworks, a challenger model runs alongside the champion on the same or comparable data so their outputs can be compared over time. Persistent or growing divergence can serve as an early signal that the champion's performance is degrading or that conditions have shifted. The specific metrics, thresholds, and cadence are generally defined by the institution's monitoring policy rather than by a single universal standard.

Who is usually responsible for building and reviewing challenger models within the lines of defense?

Responsibilities vary by organization. A challenger developed by the model owner or development team typically sits within the first line of defense, while independent review or independently constructed benchmark models are often associated with second-line functions such as model risk management or validation. The key governance concern is maintaining appropriate independence so the challenger provides a meaningful check rather than restating the champion's assumptions.

What should be documented when a challenger model is deployed?

Documentation commonly includes the challenger's purpose and scope, its design and data relative to the champion, the comparison metrics and thresholds used, the frequency of comparison, and how results feed into monitoring or promotion decisions. Clear records of who built and reviewed the challenger, and what actions its results can and cannot trigger, help preserve the distinction between benchmarking and formal change management.

What are common pitfalls when implementing challenger models?

Frequent pitfalls include building a challenger that shares the champion's data, assumptions, or blind spots and therefore provides little independent signal; comparing models on metrics that do not reflect the intended use; and treating a favorable challenger result as sufficient justification to promote it without full validation and governance sign-off. Challenger comparisons reduce and surface risk but do not eliminate it, and their limitations should be stated in the supporting documentation.

Common misconceptions

A challenger model automatically replaces the champion once it performs better on some metric.

Outperformance on a metric is typically an input to a governed decision, not an automatic trigger. Replacing a production model commonly requires additional review, validation, documentation, and approval within the institution's model risk and governance processes.

Running a challenger model constitutes independent model validation.

Challenger modeling and validation are distinct activities that can overlap but should not be collapsed. Benchmarking against a challenger is one technique that may support monitoring or validation, but validation as commonly framed involves broader assessment of conceptual soundness, ongoing monitoring, and outcomes analysis. A challenger comparison alone does not necessarily satisfy validation expectations.

A challenger model reduces or eliminates model risk on its own.

A challenger is a tool for detecting and comparing risk characteristics; it helps surface potential weaknesses in the champion but does not by itself eliminate model risk. It is a measure that can support risk management rather than a control that removes residual risk.

Best practices

Design the challenger to differ meaningfully from the champion in methodology, variables, or assumptions, so the comparison can expose weaknesses rather than merely confirm the incumbent.

Define in advance the metrics, thresholds, and comparison criteria used to evaluate champion versus challenger, and document them before drawing conclusions.

Treat challenger comparison as one input to a governed decision process for investigation, recalibration, or replacement, rather than as an automatic promotion mechanism.

Document the scope and limitations of the challenger explicitly, clarifying what the comparison demonstrates and what it does not, and how it relates to (but does not replace) formal validation.

Integrate challenger results into ongoing monitoring so that persistent underperformance of the champion prompts appropriate review under the institution's model risk management and governance procedures.

Ensure the roles of those developing, running, and reviewing the challenger are consistent with the organization's lines-of-defense structure and independence expectations.