What AI Concepts Must Risk Analysts Understand?

AI is increasingly embedded in the systems risk analysts are asked to oversee — from credit decisioning and fraud detection to trade surveillance and claims triage.

Regulators are raising expectations around AI governance, yet many risk teams have not been given a structured grounding in the concepts needed to ask the right questions.

Without this baseline literacy, risk analysts either defer entirely to technical teams, losing effective oversight, or apply traditional model risk thinking without adapting it to AI's distinct failure modes.

This article sets out the core AI concepts every risk analyst should understand — not as a technical course, but as a practical vocabulary for oversight.

Why this matters now

AI is no longer confined to experimental pilots.

It now sits inside credit decisioning engines, fraud detection systems, trade surveillance tools and claims triage processes across London's financial markets.

As these systems move from experimentation into everyday operational use, risk analysts are being asked to provide the same quality of oversight and challenge that they would apply to any other model or control.

The difficulty is that many risk teams have not been given a structured grounding in how AI systems behave, fail or differ from the systems they are used to assessing.

Regulatory expectations are also rising. Supervisors increasingly expect firms to demonstrate that risk and compliance functions understand the AI systems they are governing, not simply defer to the technical teams that built them.

This creates a gap. Without foundational AI literacy, risk analysts either step back from meaningful oversight or apply traditional model risk thinking unmodified — missing failure modes that are specific to AI.

How risk teams have traditionally approached model oversight

Risk and compliance functions already have a strong foundation to build on.

Model risk management frameworks, the three lines of defence, and established validation and governance processes have long been used to oversee deterministic, rules-based systems — where a defined input reliably produces a defined output, and the logic can be traced step by step.

These frameworks ask sensible, transferable questions: Is the model fit for purpose? Is it performing as expected? Is it subject to appropriate independent review? Are outputs monitored over time?

This traditional approach works well for systems built on fixed rules and known logic. But it assumes a certain kind of predictability — that a model's behaviour, once validated, will remain stable until deliberately changed.

Many AI systems, particularly those based on machine learning, do not behave this way. Their internal logic is learned from data rather than explicitly programmed, and their behaviour can shift as the data they encounter changes. Traditional oversight questions remain necessary, but they are not sufficient on their own.

Where AI concepts extend traditional risk thinking

A small number of concepts explain most of the difference between overseeing AI systems and overseeing traditional deterministic systems.

Machine learning versus rules-based systems. A traditional rules-based system follows explicit, human-written logic: if a transaction meets condition X, apply outcome Y. A machine learning system instead learns patterns from historical data and applies those patterns to new cases. This means its behaviour cannot always be read directly from a set of written rules — it must be understood through its training data, its performance over time and its outputs.

Model drift. Because machine learning systems learn from data, their performance can change as real-world conditions shift, even if nothing about the model itself has been altered. This is model drift. It is often silent — there is no error message, simply a gradual change in accuracy or behaviour — which is why ongoing monitoring matters more than a one-off validation exercise.

Explainability and interpretability. These two related concepts describe how well a model's behaviour can be understood and justified. Interpretability refers to understanding how a model works internally. Explainability refers to being able to justify a specific decision to a customer, auditor or regulator, even if the underlying model is complex. Both matter, but they answer different questions.

Algorithmic bias. A model trained on historical data can learn and reproduce patterns of unfair treatment present in that data, even without anyone intending this outcome. Bias is a distinct risk category from simple inaccuracy, and it requires its own specific checks.

Hallucination. In generative AI systems — tools that produce text, summaries or narrative outputs — hallucination describes the system confidently producing information that is plausible but factually incorrect or fabricated. This failure mode has no direct equivalent in traditional deterministic systems and requires new verification habits.

Human-in-the-loop oversight. This describes designing a process so a person reviews, challenges or approves an AI system's output before it takes effect, rather than allowing the system to act entirely autonomously. It is one of the most practical controls available to a risk function, and understanding where it is present — or absent — in a given process is a core oversight question.

None of these concepts replace existing model risk frameworks. They extend them, adding the specific questions needed to oversee systems that learn and change rather than simply execute fixed logic.

Embedding this literacy across the risk function

Understanding these concepts individually is only the starting point.

Risk functions benefit from agreeing a shared glossary of AI terms across the first, second and third lines of defence, so that "model drift" or "explainability" means the same thing whether it is raised by an underwriting team, a risk analyst or an internal audit function.

This literacy should not be treated as a one-off training event. As AI use cases evolve within the organisation, the concepts that matter most will shift too — a firm piloting its first generative AI tool will need different emphasis than one already running several machine learning models in production.

It is also important to be clear about the limits of this literacy. Understanding these concepts equips a risk analyst to ask better questions and recognise when something warrants escalation. It does not replace the need for technical model validation specialists, who remain essential for deep technical assessment. The goal is informed, proportionate challenge — not technical self-sufficiency.

Example

A London-based commercial insurer introduces an AI-assisted claims triage tool that flags high-risk claims for further investigation.

Six months after deployment, a risk analyst reviewing quarterly model performance data notices the tool's flagging rate for a particular claim category has quietly shifted.

Rather than dismissing the change or escalating without justification, the analyst recognises the pattern as a possible case of model drift and asks a precise, well-framed question: does this reflect a genuine change in underlying risk, or a deterioration in the model's performance as claim submission patterns have evolved?

This question triggers a targeted review by the model risk and validation team, which is able to distinguish a genuine data shift from a deteriorating model.

The analyst did not need to be a data scientist to add real value — only to understand what model drift is and why it mattered.

FAQs

• Do risk analysts need to learn to code or build AI models?

  No. AI literacy for risk oversight is about conceptual understanding and asking the right questions, not technical model-building skills. Building and training models remains the domain of data science and model risk specialists — the analyst's role is to provide informed, proportionate challenge.

• How is model drift different from a traditional model becoming outdated?

  A traditional model typically becomes outdated through identifiable changes, such as new products or market conditions, and is addressed through periodic revalidation. Model drift in AI systems can be more subtle and continuous, arising as the system continues to learn from or encounter evolving real-world data, which is why ongoing monitoring matters alongside periodic review.

• What is the difference between explainability and interpretability?

  Interpretability relates to understanding how a model works internally — its logic and structure. Explainability relates to being able to justify a specific decision to a stakeholder, customer or regulator, even where the underlying model is complex. Both matter for risk oversight, but they answer different questions.