Fraud prevention has always involved identifying patterns. What has changed is the scale, speed and complexity of the information available to fraud teams, and the ability of artificial intelligence and machine learning to analyse those signals in real time.
A digital interaction might generate information about a customer’s device, location, identity, transaction history and behaviour. Examining each signal manually would be impractical, particularly when thousands or millions of interactions are taking place.
AI fraud detection software can analyse combinations of signals to identify relationships, anomalies and behaviours associated with fraud, helping organisations make faster risk decisions.
But AI is not a magic fraud detector.
Models depend on appropriate data, require monitoring and can generate false positives. Fraud teams also need to understand how automated decisions fit alongside existing rules, manual investigations and customer-experience requirements.
This guide examines what fraud and risk professionals should compare when evaluating AI fraud prevention platforms and specialist technology providers.
At a Glance: What AI Fraud Prevention Buyers Should Compare
| Capability | What Buyers Should Consider |
|---|---|
| Data | Which fraud and behavioural signals are analysed |
| Machine learning | How models identify patterns and anomalies |
| Real-time scoring | How quickly risk can be assessed |
| Device intelligence | Recognition of suspicious devices and environments |
| Identity | Verification, biometrics and synthetic identity detection |
| Behaviour | Detection of unusual customer activity |
| Rules | Ability to combine AI with configurable controls |
| Explainability | Visibility into why something was flagged |
| False positives | Impact on legitimate customers |
| Integration | Connection with payments, identity and case-management systems |
What Is AI Fraud Detection?
AI fraud detection uses technologies including machine learning to analyse data and identify activity that may indicate fraud.
Traditional fraud systems frequently rely heavily on predefined rules.
For example:
IF transaction > £5,000 AND country = high risk → REVIEW
Rules remain extremely useful because they allow fraud teams to encode known risks and business policies.
But criminals adapt.
Machine-learning models can analyse larger combinations of signals and identify relationships that may be difficult to express through individual rules.
A transaction might appear ordinary when assessed against any single factor, but become suspicious when several characteristics are considered together.
The aim is therefore not necessarily to replace rules with AI.
It is to create a more sophisticated decisioning environment combining:
Rules + machine learning + contextual data + human expertise
AI Fraud Detection Software
Modern AI fraud detection software can operate at multiple stages of the customer journey:
Account creation → login → transaction → account change → withdrawal → account recovery
Potential capabilities include:
- Machine-learning risk scoring
- Anomaly detection
- Device intelligence
- Behavioural analysis
- Identity verification
- Biometrics
- Network analysis
- Digital footprint analysis
- Automated decisioning
- Case prioritisation
SEON, for example, combines digital-footprint information, device intelligence, behavioural data and AI scoring to support real-time fraud decisions.
SEON – Fraud Prevention – https://seon.io/products/fraud-prevention/
Mastercard’s Decision Intelligence Pro similarly uses transaction context, cardholder behaviour and relationships between entities to support real-time authorisation decisions.
Mastercard – Decision Intelligence – https://www.mastercard.com/global/en/business/cybersecurity-fraud-prevention/risk-decisioning/decision-intelligence.html
For buyers, the important question is not simply whether a supplier “uses AI”.
It is what the AI analyses, which decisions it informs and how effectively those decisions can be controlled and evaluated.
AI vs Rules-Based Fraud Detection
Rules and machine learning solve different problems.
Rules
Rules are particularly useful when the organisation knows precisely what it wants to detect.
Examples might include:
- Transactions above a threshold
- Activity from prohibited locations
- Excessive login attempts
- Known compromised credentials
- Policy violations
They are transparent and relatively easy to understand.
But large rule libraries can become difficult to maintain, while new fraud patterns may not match existing conditions.
Machine learning
Machine-learning models can analyse complex combinations of data and identify patterns based on previous examples.
They can potentially identify activity that does not trigger any single predefined rule.
However, models require suitable data, monitoring and governance.
Fraud Prevention Principle
The choice does not have to be:
Rules OR AI
A stronger question is:
“Which decisions should be deterministic, which benefit from machine learning, and where should human judgement remain involved?”
The Importance of Fraud Data
AI models are only as useful as the signals available to them.
Potential fraud data includes:
Transaction data
- Amount
- Product
- Payment method
- Frequency
- Previous activity
Device data
- Device characteristics
- Browser
- Operating system
- Network
- VPN or proxy usage
- Device history
Identity data
- Name
- Address
- Telephone
- Identity documents
- Biometrics
Behavioural data
- Navigation
- Interaction patterns
- Login behaviour
- Transaction behaviour
- Velocity
Historical outcomes
- Confirmed fraud
- Chargebacks
- Genuine transactions
- Manual-review decisions
The objective is to build enough context to distinguish suspicious behaviour from legitimate variation.
Device Intelligence and AI Fraud Prevention
Device intelligence has become an important input into digital fraud prevention.
A platform may examine whether:
- A device has been seen before
- Multiple accounts use the same device
- A browser has been manipulated
- Automation is present
- A VPN or proxy is being used
- The device environment appears suspicious
Fingerprint, for example, provides device intelligence through its Smart Signals, which can identify indicators including bots, VPN usage, browser tampering and other suspicious device characteristics.
Fingerprint – Smart Signals – https://docs.fingerprint.com/docs/smart-signals-introduction
This information can then contribute to a broader risk decision.
Risk Insight
A suspicious signal should not necessarily equal an automatic decline.
A legitimate customer may use a VPN.
The value comes from assessing that signal in context with other information.
AI Risk Scoring
Many fraud platforms convert multiple signals into a risk score.
For example:
Low risk → approve
Medium risk → additional verification
High risk → review or decline
This allows organisations to create different responses according to risk rather than treating every customer identically.
SEON’s current AI Insights score, for example, calculates a real-time fraud probability using device, behavioural, velocity, consortium and digital-footprint signals.
SEON – AI Insights Score – https://docs.seon.io/knowledge-base/machine-learning/ai-insights-score-and-explanation
Buyers should investigate:
- What contributes to the score?
- How is it calibrated?
- Can thresholds be changed?
- How frequently is the model updated?
- Can different thresholds apply to different use cases?
- What happens when the score is uncertain?
The number itself is less important than understanding how the organisation should act on it.
Real-Time Fraud Detection
Fraud decisions often need to happen within seconds or milliseconds.
Examples include:
- Account opening
- Login
- Card payment
- Withdrawal
- Password reset
A system that identifies fraud several hours later may still support investigation, but the financial loss may already have occurred.
Buyers should therefore understand the distinction between:
- Real-time prevention
- Near-real-time monitoring
- Retrospective analysis
Latency also matters.
Adding multiple fraud checks can improve risk assessment but potentially slow customer journeys.
The objective is to make sufficiently informed decisions within the time available.
Identity Verification and AI
AI also plays an important role in determining whether someone is who they claim to be.
Identity verification can involve:
- Document verification
- Facial biometrics
- Liveness detection
- Behavioural analysis
- Device information
- Database checks
Mitek’s identity technology, for example, combines automated machine learning with document analysis, biometrics and liveness capabilities to support identity verification and fraud prevention.
Mitek – Identity Verification – https://www.miteksystems.com/identity-verification
This is becoming particularly important as fraudsters themselves gain access to increasingly sophisticated AI tools.
AI Fighting AI-Enabled Fraud
Generative AI has created new challenges for fraud teams.
Potential attacks include:
- Deepfakes
- Synthetic identities
- Manipulated documents
- AI-generated identity documents
- Voice impersonation
- Injection attacks
- Automated social engineering
Mitek highlights deepfakes, synthetic identities and injection attacks among the emerging identity threats its AI-based fraud defences are designed to detect.
Mitek – Digital Fraud Defender – https://www.miteksystems.com/products/digital-fraud-defender
The resulting dynamic is important.
AI is not only changing fraud prevention.
It is changing fraud itself.
Organisations therefore need controls capable of adapting as attack techniques evolve.
Detecting Anomalies
One of machine learning’s strengths is its ability to identify behaviour that differs from expected patterns.
Imagine a customer who normally:
- Logs in from the UK
- Uses one device
- Makes purchases below £100
- Shops during daytime hours
Suddenly the account:
- Uses a new device
- Connects through an unusual network
- Changes contact details
- Attempts a £2,000 transaction
None of these factors alone necessarily proves fraud.
Together they may justify additional scrutiny.
This is where combining multiple signals can become significantly more useful than relying on isolated rules.
Adaptive Fraud Prevention
Fraud patterns change.
A model trained on last year’s fraud may become less effective if criminal behaviour changes significantly.
Buyers should therefore investigate how models adapt.
Questions include:
- How frequently are models retrained?
- Which data is used?
- Can customer-specific fraud outcomes improve performance?
- How quickly can new fraud patterns be incorporated?
- How is model performance monitored over time?
Feedback is particularly important.
When an organisation confirms that an event was fraudulent or legitimate, that outcome can potentially improve future detection.
Explainable AI in Fraud Detection
One of the biggest procurement issues is explainability.
A system might produce:
Risk score: 94
But why?
Fraud analysts may need to understand whether the score resulted from:
- Device anomalies
- Transaction behaviour
- Identity mismatch
- Velocity
- Network relationships
- Previous fraud
SEON, for example, provides AI summaries designed to explain influential factors behind transactions and alerts, including device fingerprints, behavioural anomalies and triggered rules.
SEON – AI and Machine Learning – https://docs.seon.io/knowledge-base/machine-learning/ai-and-machine-learning
Explainability helps analysts investigate cases and can also support model governance.
Governance Principle
A fraud score should support a decision, not become a substitute for understanding the decision.
Buyers should be wary of systems where nobody can meaningfully explain why important outcomes occur.
False Positives and Customer Friction
Fraud prevention has two costs:
Fraud that gets through
and
legitimate customers incorrectly treated as fraudsters.
An aggressive model might reduce fraud while also increasing:
- Declined transactions
- Account lockouts
- Additional authentication
- Manual reviews
- Customer-service contacts
This creates a balancing problem.
Mastercard positions reduced false declines as one of the objectives of its Decision Intelligence technology, alongside improved fraud-detection accuracy.
Mastercard – Decision Intelligence – https://www.mastercard.com/global/en/business/cybersecurity-fraud-prevention/risk-decisioning/decision-intelligence.html
Buyers should therefore evaluate fraud performance alongside customer outcomes.
Step-Up Verification
Not every suspicious interaction needs to be blocked.
An alternative is step-up verification.
For example:
Low risk → continue normally
Moderate risk → request additional authentication
High risk → block or manually review
Additional checks might include:
- One-time passcode
- Biometric verification
- Identity document
- Additional account authentication
This creates an adaptive customer journey.
Trusted users encounter minimal friction while higher-risk interactions receive stronger controls.
Network and Relationship Analysis
Fraud is often connected.
Multiple apparently unrelated accounts might share:
- Devices
- Addresses
- Telephone numbers
- Payment instruments
- IP addresses
- Behavioural patterns
AI and graph technologies can help identify these relationships.
Mastercard’s Decision Intelligence Pro, for example, uses relational risk and graphing techniques to examine relationships between entities rather than considering a transaction solely in isolation.
This can be valuable for detecting organised fraud where individual transactions appear relatively normal but the wider network reveals suspicious patterns.
Human Oversight
AI can process far more data than a fraud analyst.
That does not make the analyst redundant.
Human teams remain important for:
- Complex investigations
- Emerging fraud patterns
- Model monitoring
- Rule creation
- Exception handling
- Customer disputes
- Strategic decisions
AI can instead help prioritise their workload.
Rather than asking analysts to investigate every alert equally, technology can help identify cases most likely to require attention.
Think Augmentation, Not Replacement
The strongest fraud operations combine machine scale with human judgement.
AI can find patterns and prioritise risk.
Experienced investigators can understand context, challenge unusual outcomes and identify new tactics that models have not yet learned.
Integrating AI Fraud Prevention
Fraud technology rarely operates alone.
Potential integrations include:
- Payment gateways
- Banking systems
- eCommerce platforms
- Identity verification
- CRM
- Authentication
- Case management
- Chargeback systems
- AML platforms
Buyers should establish where the fraud platform sits within the decision flow.
For example:
Customer action → data collection → risk assessment → decision → verification/review → outcome feedback
Integration design can have a major impact on both detection and customer experience.
Testing AI Fraud Detection Software
Supplier demonstrations can make AI fraud detection appear remarkably simple.
Real-world testing is more important.
Where possible, organisations should evaluate technology using representative historical or controlled data.
Measures might include:
- Fraud detection rate
- False-positive rate
- False-negative rate
- Precision
- Recall
- Manual-review rate
- Decision latency
- Customer friction
Buyers should also test different fraud types rather than relying on one overall performance number.
A platform may perform exceptionally well against payment fraud but less effectively against account takeover or synthetic identity fraud.
Measuring AI Fraud Prevention
Potential measures include:
Fraud loss rate
How much fraud gets through?
Detection rate
How much known fraud does the system identify?
False-positive rate
How many legitimate interactions are incorrectly flagged?
Manual-review rate
How many cases require human intervention?
Approval rate
Are legitimate customers successfully completing transactions?
Decision speed
How quickly is risk assessed?
Investigation efficiency
Can analysts resolve cases faster?
Customer friction
How frequently are legitimate users subjected to additional checks?
The objective should be to evaluate the overall fraud-control system, not simply the accuracy of an AI model in isolation.
What Should Buyers Compare?
Data coverage
Which signals can the platform analyse?
Machine learning
What models are used and how are they trained?
Real-time capability
How quickly are decisions returned?
Device intelligence
Can suspicious devices, bots and manipulation be identified?
Identity
What verification and authentication capabilities are available?
Explainability
Can analysts understand why activity was flagged?
Rules
Can fraud teams retain control through configurable rules?
False positives
How does the platform protect legitimate customer journeys?
Integration
How easily does it connect with existing systems?
Model governance
How is performance monitored and updated?
Questions to Ask AI Fraud Prevention Suppliers
- Which fraud types does your platform specialise in?
- Which data signals does the AI analyse?
- What machine-learning models are used?
- How are models trained?
- Can our own fraud outcomes improve the models?
- How frequently are models updated?
- Can we configure risk thresholds?
- Can AI scores be combined with our own rules?
- How quickly are decisions returned?
- How do you detect new or previously unseen fraud?
- What device intelligence is available?
- How do you detect bots and automation?
- What identity-verification capabilities are supported?
- Can you detect deepfakes and synthetic identities?
- Can analysts understand why a transaction was flagged?
- How do you measure false positives?
- Can higher-risk users be routed to step-up verification?
- Which systems do you integrate with?
- How do you monitor model drift and performance?
- What reporting and case-management tools are available?
Frequently Asked Questions
What is AI fraud detection?
AI fraud detection uses artificial intelligence and machine learning to analyse data for patterns, anomalies and relationships that may indicate fraudulent activity.
How does machine learning detect fraud?
Machine-learning models analyse examples and data patterns to estimate whether new activity resembles legitimate or fraudulent behaviour.
Is AI better than fraud rules?
They perform different functions. Rules are useful for known and clearly defined conditions, while machine learning can identify more complex patterns. Many fraud-prevention strategies combine both.
Can AI detect new fraud?
Machine learning can help identify unusual patterns and anomalies that may not match existing rules, although no system can guarantee detection of every new fraud technique.
What is an AI fraud risk score?
It is an assessment of the likelihood that an interaction or transaction presents fraud risk, based on signals analysed by a model.
Can AI reduce false positives?
Potentially. Analysing a wider range of contextual signals can help distinguish suspicious behaviour from legitimate activity, but performance depends on the data, models and configuration.
Can AI detect AI-generated fraud?
Specialist technologies increasingly use AI and machine learning to detect threats including manipulated documents, deepfakes, injection attacks and synthetic identities.
Product Guide
AI fraud prevention spans device intelligence, payment decisioning, identity verification, behavioural analysis and real-time risk scoring. Buyers should compare providers according to the particular fraud problems and customer journeys they need to protect – the below solution providers can all be met at the Fraud Prevention Summit.
Featured Suppliers
Fingerprint
Device-intelligence platform helping organisations identify web and mobile visitors and detect suspicious activity associated with fraud, account takeover, bots and payment abuse. Its Smart Signals provide information including bot, VPN and browser-tampering detection, which can feed into fraud models and risk decisioning.
Website: https://www.fingerprint.com/
Mastercard
Global payments and technology company providing fraud, cybersecurity and risk-decisioning capabilities across the payments ecosystem. Its Decision Intelligence technology uses AI, transaction context, cardholder behaviour and relationship analysis to support real-time authorisation decisions and help identify fraud while reducing unnecessary declines.
Website: https://www.mastercard.com/
Mitek
Digital identity and fraud-prevention specialist using AI, machine learning, document verification, biometrics and liveness technology to help organisations establish identity and combat fraud. Its capabilities address threats including synthetic identity fraud, account takeover, deepfakes and injection attacks across the customer lifecycle.
Website: https://www.miteksystems.com/
SEON Technologies Ltd
Fraud prevention and AML technology provider combining digital-footprint analysis, device intelligence, behavioural data, rules and AI-powered scoring. Its platform supports real-time risk decisions across use cases including account creation, login, payments and withdrawals, alongside tools designed to help analysts understand the signals influencing fraud decisions.
Website: https://seon.io/
Explore AI Fraud Prevention Solutions
AI is giving fraud teams the ability to analyse more signals and relationships at greater speed than would be possible manually.
But successful AI fraud prevention is not simply about deploying the most sophisticated algorithm.
The strongest approach combines high-quality data, appropriate machine learning, configurable rules, explainability and experienced human investigators.
Buyers should therefore look beyond claims about AI accuracy and examine how the technology will operate within their actual fraud environment: what information it uses, how quickly it acts, why it makes particular decisions and what those decisions mean for legitimate customers.
The Fraud Prevention Summit connects senior fraud, risk and security professionals with carefully selected technology and service providers through a programme of pre-arranged one-to-one meetings.
Explore AI fraud prevention solutions, compare specialist providers and discover technologies that can help organisations identify emerging threats, make faster risk decisions and reduce fraud without introducing unnecessary customer friction.
Sources
- Fingerprint – https://fingerprint.com/
- Fingerprint – Smart Signals – https://docs.fingerprint.com/docs/smart-signals-introduction
- Mastercard – Decision Intelligence – https://www.mastercard.com/global/en/business/cybersecurity-fraud-prevention/risk-decisioning/decision-intelligence.html
- Mitek – Identity Verification – https://www.miteksystems.com/identity-verification
- Mitek – Digital Fraud Defender – https://www.miteksystems.com/products/digital-fraud-defender
- SEON – Fraud Prevention – https://seon.io/products/fraud-prevention/
- SEON – AI and Machine Learning – https://docs.seon.io/knowledge-base/machine-learning/ai-and-machine-learning
- SEON – AI Insights Score – https://docs.seon.io/knowledge-base/machine-learning/ai-insights-score-and-explanation
Image credit: https://unsplash.com/photos/diverse-team-collaborating-around-a-computer-screen-Y32qbykD69g
