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AI is not automatically more accurate or less explainable than a traditional financial risk model. Machine-learning approaches can find complex patterns in varied data, while conventional statistical and quantitative models often use more explicit assumptions. Which is more useful depends on the task, the data, the evidence from testing and the controls around deployment. Both need sound validation and ongoing oversight.
What is the difference between AI and traditional risk models?
“AI” covers a range of techniques, including machine learning (ML); it is not one model type. Traditional approaches are not all alike either. Generalized linear models (GLMs) and internal ratings-based (IRB) approaches are examples of conventional statistical or quantitative methods, but the distinction between “traditional” and “AI” is a tendency, not a clean boundary. The Bank of England’s 2022 discussion paper describes potential applications and risks across AI and ML in financial services (DP5/22).
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| Dimension | Traditional statistical or quantitative approaches | AI and ML approaches |
|---|---|---|
| Construction | Often begin with a specified form, assumptions and fixed parameterisation. | Can learn relationships and parameterisations iteratively from data. |
| Inputs | Often use selected, structured variables. | Can use more features and varied sources, potentially including unstructured text or images. |
| Relationships and change | A specified form may be easier to inspect, but can miss nonlinear or complex relationships if its assumptions do not fit. Parameters may change less frequently. | Flexible methods may capture complex patterns and may be updated more frequently; some systems learn continuously. |
| Explainability | Some methods are comparatively interpretable, but conventional GLMs and regulatory capital approaches can also be complex and difficult to explain. | Some complex methods are opaque or difficult to audit; explainability depends on the actual method and how it is used. |
| Risk-management work | Requires review of conceptual soundness, inputs and assumptions, performance testing and monitoring. | Requires those same disciplines, with additional attention to representativeness, drift, complexity, updates, explainability and governance. |
This comparison does not establish which approach will perform better. A model that fits training data well may not work as well on new cases or under changed conditions, so performance evidence needs to reflect the intended use.
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ML may help process information, support operational efficiency and model relationships that are difficult to capture with a specified form. Supervisory material describes potential applications such as predicting credit default risk and processing insurance underwriting or claims. Those are possible benefits, not guaranteed gains: results depend on the data, model design, validation and deployment setting (Bank of England, PRA and FCA, DP5/22).
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The comparison changes with the risk task. A credit-risk model, for example, must be judged on the decision it supports and the quality and relevance of its inputs; the same model or evidence would not automatically establish suitability for insurance underwriting, market risk or operational risk. Data available, the decision being made and the consequences of errors differ across these settings. Assess the particular model in its intended context rather than choosing a category in the abstract.
Are AI risk models more accurate?
There is no basis here for saying AI is categorically more accurate across finance or across datasets. Compare candidate models on the same relevant task and data, and distinguish performance on unseen data from a result measured only on the data used to fit the model. Out-of-sample and out-of-time tests can help show whether performance holds beyond the training period; comparison with alternative methods can show whether added complexity delivers a meaningful benefit.
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A useful evaluation asks whether the model’s outputs support the intended decision, whether its performance is stable under changing conditions, and whether errors have been examined in ways relevant to the risk. One performance metric alone does not establish that a model is safe or fit for use. Supervisory guidance emphasizes model soundness, testing and risk-based oversight rather than assuming a winner by model family (U.S. interagency model-risk guidance).
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Are traditional models easier to explain?
Sometimes, but not by definition. A method with an explicit form and selected inputs may be easier to inspect than a complex method, yet some conventional GLMs and IRB approaches are also difficult to explain. Conversely, the label “AI” alone does not tell you whether a particular method can be understood, audited or challenged. Evaluate the actual model and the decision it informs.
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For a consequential decision, useful governance needs more than a general description of a model family. Reviewers need to understand the relevant inputs, assumptions and limitations well enough to assess how the result was produced and whether it should be relied on. The Financial Stability Board warned in 2017 that “The lack of interpretability or auditability of AI and machine learning methods could become a macro-level risk.” (FSB report).
What risks need attention across an AI model’s lifecycle?
- Data quality and representativeness: Incomplete or inaccurate inputs can weaken estimates. Historically biased data can carry unfair patterns into model outputs; more sources or features do not guarantee more relevant evidence.
- Drift and change control: If relationships or input populations change, performance may deteriorate. Frequent updates or continuous learning make it more challenging to validate versions, track changes and determine when a model needs review.
- Explainability and challenge: Complexity can make it harder to audit a model or explain an individual outcome, which can hinder effective review and challenge.
- Human accountability: Automation can weaken oversight if responsibility for monitoring outputs, handling exceptions and acting on failures is unclear.
- Third-party and shared-model dependence: Reliance on common providers, data libraries or model components can create dependencies and concentration.
- System-wide correlation: Firms using similar data or algorithms may make more correlated decisions, while a shared model defect could lead many firms to mismeasure risk at once. The Bank of England and FSB identify these types of dependencies, correlations, cyber risk, model risk and data-quality or governance issues as concerns to monitor (Bank of England, April 2025; FSB, November 2024).
These are connected control problems, not reasons to assume every AI model is unsafe. They make governance, monitoring and clear ownership part of the model’s design and operation, rather than tasks to consider only after deployment.
How should a financial firm compare the approaches?
- Define the decision and risk. Specify what the model will inform, who will use it and what errors would mean in that setting.
- Check the data before the model. Assess input accuracy, completeness, relevance and representativeness for the population and period in which the model will be used.
- Compare methods on appropriate evidence. Use out-of-sample and, where relevant, out-of-time testing; compare with alternative methods and examine whether observed gains hold beyond training results.
- Review explainability and challenge. Determine whether reviewers can inspect the model and its limitations sufficiently for the decision’s consequences.
- Plan validation and monitoring. Set out how performance, drift, model updates and data quality will be monitored, and how versions and changes will be controlled.
- Assign responsibility. Make ownership for approval, exceptions, oversight and responding to problems explicit, including where third parties are involved.
The 2022 UK survey offers a bounded illustration of governance practices, not a current adoption estimate: among surveyed UK financial-services respondents using ML, 80% said their applications had data-governance frameworks and 67% said model-risk and operational-risk frameworks were in place (Bank of England and FCA, 2022).
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Regulatory material is jurisdiction- and firm-specific. It should not be read as a universal approval of AI techniques or as a complete answer to every question about generative or autonomous systems.
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United States
On 17 April 2026, the Federal Reserve Board, OCC and FDIC issued revised supervisory guidance on model risk management, superseding the older SR 11-7 guidance. It takes a risk-based approach tailored to model risk, organizational scale and complexity. The guidance applies its principles to traditional statistical and quantitative models and to non-generative, non-agentic AI models; generative and agentic AI are outside its scope. It is most relevant to banking organizations with more than $30 billion in assets, though it may also be relevant to smaller organizations with significant model risk. It is supervisory guidance, not a universal prescriptive rule (Federal Reserve Board, OCC and FDIC).
United Kingdom
The current version of PRA Supervisory Statement SS1/23 was published and became effective on 23 April 2026. Its five principles address model identification and risk classification; governance; development, implementation and use; independent validation; and model-risk mitigants. It is relevant to specified UK-incorporated banks, building societies and PRA-designated investment firms with internal model approval for regulatory capital calculations—not all UK financial firms. Its principles are technology-neutral and include managing AI/ML risks where they apply to model use (PRA SS1/23).
What this means in practice
Established model-risk, data, governance and conduct controls address many risks associated with AI, while supervisors continue to assess whether existing frameworks are sufficiently comprehensive. The cited guidance does not resolve every question about generative AI or autonomous agents; its scope and the rules applicable to a particular firm matter.
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