Artificial intelligence (AI) is transforming fraud prevention across the payments sector. Machine learning models are helping firms identify suspicious activity faster and more accurately than traditional rule-based systems, adapting to new fraud typologies as they emerge.
However, as adoption grows, the questions regulators are asking is changing. The challenge is no longer whether AI can detect fraud. It is whether firms can explain how automated decisions are reached, govern those models effectively and evidence that oversight in practice.
Why is AI fraud detection different from traditional fraud monitoring?
Traditional fraud monitoring systems relied on relatively simple rules. Transactions were flagged based on predefined thresholds, geographic indicators or known fraud typologies. Each flagged transaction had a traceable reason behind it.
AI-driven systems operate differently. Rather than following a fixed set of rules, machine learning models identify patterns within large datasets and adapt as new information becomes available. This improves detection significantly, but it also makes decision-making harder to trace.
If a customer asks why a payment was blocked, or a regulator asks how a particular decision was reached, firms need a clear and defensible explanation. Strong outcomes alone are unlikely to be sufficient if the decision-making process cannot be understood or evidenced.
Consider a simple example. A payment is blocked because an AI model identifies unusual behaviour. The customer challenges the decision. Can the firm explain which factors influenced the outcome, what data was used and whether a manual review took place? If not, the issue is no longer just fraud detection – it is governance and explainability.
In our experience, this is where many firms are exposed. Across recent client reviews, we’ve found that firms often invest heavily in model performance and detection rates but give far less attention to whether a decision can be reconstructed after the fact. When the model works well, nobody asks the question. When a customer complains or a regulator investigates, the gap becomes obvious.
How does Consumer Duty apply to AI fraud detection?
Consumer Duty adds a further dimension.
Fraud prevention is clearly intended to deliver good customer outcomes, but firms must also consider the potential impact of automated decision-making on customers.
False positives remain a persistent challenge. A legitimate payment that is delayed or blocked can create significant customer frustration and, in some circumstances, financial harm.
Firms should think about whether their fraud controls create unnecessary friction, how clearly decisions are communicated, whether vulnerable customers are adequately supported, and how easily a customer can challenge or query an outcome. The goal is not simply to prevent fraud; it is to balance effective controls with fair customer treatment.
Who is responsible for AI-driven fraud decisions?
The introduction of AI does not remove accountability.
Whether a decision is made by a member of staff, a traditional monitoring system or a machine learning model, responsibility ultimately remains with the firm.
As AI becomes more embedded within fraud frameworks, firms should ensure they have appropriate governance arrangements in place. This means clear ownership of model performance, ongoing monitoring and validation, defined escalation and challenge processes, and management information that gives the board genuine oversight rather than a summary dashboard. The sophistication of a model should not outpace the firm’s ability to govern it effectively.
What does good AI governance look like?
Governance
Board oversight includes meaningful challenge rather than simply reporting fraud rates.
Explainability
Key model decisions can be reconstructed and explained to customers, auditors and regulators.
Customer outcomes
False positives are monitored and customer communications are regularly reviewed.
Auditability
Model decisions, data inputs and manual overrides are retained through a clear audit trail.
Why explainability and audit trails matter
Auditability is often the area most overlooked. When a complaint, regulatory review or financial crime investigation occurs, firms need to be able to demonstrate how a decision was made. This means maintaining sufficient records to answer questions such as:
- What data informed the decision?
- Which model was used?
- What factors contributed to the outcome?
- Were any manual interventions applied?
Without clear documentation and audit trails, firms may struggle to evidence that decisions were reasonable, consistent and appropriately controlled.
How should firms prepare for AI-driven fraud detection?
Firms deploying or reviewing AI-driven fraud detection should:
- Map current model use across the fraud framework and identify where explainability gaps exist.
- Review governance arrangements against ownership, escalation and management information expectations.
- Test whether a sample decision could be reconstructed and explained to a customer or regulator today.
- Assess the customer journey for false positives, including how clearly outcomes are communicated.
- Retain evidence of model decisions and any manual overrides as a matter of course, not only when a complaint arises.
What should firms expect next?
AI has the potential to strengthen fraud prevention as threats continue to evolve.
However, regulatory expectations are likely to extend beyond model performance alone. Governance, oversight and explainability will become increasingly important as firms deploy more advanced technologies within their financial crime frameworks.
The firms that gain the greatest value from AI will not necessarily be those with the most sophisticated models. They will be those that can demonstrate they understand, govern and evidence the decisions those models make.
In the long term, being able to explain a decision may prove just as important as making the right one.
How fscom can help
fscom supports payment firms, e-money institutions and fintechs in assessing governance frameworks, financial crime controls and Consumer Duty obligations.
We help firms evaluate emerging technologies, strengthen oversight arrangements and ensure innovation is supported by appropriate governance and regulatory compliance.
Whether you are implementing AI-driven fraud detection or reviewing existing governance arrangements, fscom can help you assess explainability, strengthen oversight and ensure innovation is supported by robust controls.
Get in touch to discuss how fscom can help.
This post contains a general summary of advice and is not a complete or definitive statement of the law. Specific advice should be obtained where appropriate.
