Real-time fraud scoring
Models score payments before release using behaviour, device and payee features, with false positive cost and customer friction weighed against losses prevented.
GRA · Financial Crime and Fraud Risk
Spatial financeWhere capital meets hazard, exposure and transition
Artificial intelligence and machine learning for financial crime and fraud risk
Artificial intelligence and machine learning are used to detect fraud, prioritise alerts, verify identities and find networks of suspicious accounts. On this platform they are treated as detection methods with error rates, biases and governance needs, and as tools criminals also use.
01Register
Artificial intelligence and machine learning for financial crime and fraud risk on the record.
Method recordSTEM method
- MethodArtificial intelligence and machine learning
- Applied inFinancial Crime and Fraud Risk
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What artificial intelligence and machine learning does here.
Artificial intelligence and machine learning develop algorithms that learn patterns from data to classify, predict or generate outputs. They are increasingly used for hazard detection, forecasting and decision support, and are themselves a source of risk. In financial stability, cyber and digital resilience and governance and institutions, machine learning can scale detection and forecasting across interconnected systems. Its own failure modes must be weighed when automated outputs inform consequential decisions. The canonical description is on the STEM Commons.
Alert prioritisation
Machine learning ranks transaction monitoring alerts so analysts review likely cases first, with validation against later outcomes and regular retuning.
Fairness in fraud controls
Block and closure rates are compared across customer groups to detect controls that fall disproportionately on particular communities without justification.
03Monitor
Model training.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Model training
Financial crime · Artificial intelligence · Run R-2085
Drift on one sliceGRA · Financial Crime and Fraud Risk
Evaluation by slice
| All | 0.90 | 0.01 | 7454 | Pass |
| Region A | 0.85 | 0.03 | 1390 | Pass |
| Region B | 0.77 | 0.08 | 873 | Review |
| Rare class | 0.75 | 0.15 | 92 | Below bar |
Method card
- Method
- Artificial intelligence and machine learning
- Split
- time-based, no leakage
- Features
- 26
- Label
- MODELLED
Population stability
04Data
What the method consumes.
Datasets and records used by artificial intelligence and machine learning on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Labelled fraud and alert outcomes | Training and testing | REPORTED |
| Payment and device data | Features | OBSERVED |
| Customer complaint data | Harm from false positives | REPORTED |
05Exposure, loss and capital
What it means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Transmission channels
How a shock in artificial intelligence and machine learning for financial crime and fraud risk reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
06Validation
How results are checked.
Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.
| Check | How it is done |
|---|---|
| Label quality | Training labels are checked for missed fraud and mislabelled cases |
| Adversarial adaptation | Models are retested as fraud patterns shift |
| Outcome fairness | Error rates are compared across groups |
Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.
07Process
How the process runs.
Process explorer · Process
Artificial intelligence and machine learning for financial crime and fraud risk: AI risk management (NIST)
9 steps·5 stages·1 gate
Process
Artificial intelligence and machine learning for financial crime and fraud risk: AI risk management (NIST)
The AI RMF Core has four functions, GOVERN, MAP, MEASURE and MANAGE, with GOVERN cross-cutting; the order drawn is one common reading, not a checklist.
01 Govern
- 01GOVERN: policies, roles and accountabilityStep · Sanctions officerGOVERN is a cross-cutting function. It is infused throughout AI risk management and enables the other functions.
02 Map
- 02MAP: context and intended useDatasetThe MAP function establishes the context to frame risks related to an AI system, including intended purposes and impacts on people.
03 Measure
- 03MEASURE: evaluate trustworthy characteristicsModelAI systems are evaluated for trustworthy characteristics. They should be tested before deployment and regularly in operation.
- 04MEASURE: track identified risks over timeModelMechanisms for tracking identified AI risks over time are in place, including unanticipated and emergent risks.
04 Manage
- 05MANAGE: treat risksStepRisk treatment plans cover response to, recovery from and communication about incidents. Resources go to mapped and measured risks.
- 06Proceed or not (MANAGE 1.1)Gate · Sanctions officerA determination is made whether the system meets its purposes and objectives, and whether to proceed. The framework sets no thresholds.
- 07Deployment decisionDecision · Sanctions officerThe organisation that owns the system decides whether and under what conditions to deploy.
- 08Post-deployment monitoring and incidentsModelPost-deployment monitoring plans are implemented. Incidents and errors are shared with relevant AI actors, including affected communities.
05 Record
- 09Inventory of AI systemsDatasetGOVERN 1.6 asks for mechanisms to inventory AI systems. A model card is common practice and is not named in the framework.
Neutral outline of a published external standard or method, from its issuing body. Not a record of operation. Nexus does not perform, certify or approve any step shown. A gate or decision belongs to the party the source names. The actions are not necessarily an ordered set of steps. The EU AI Act is not drawn in this flow.
08Connections
Topics that rely on this method.
09Stewardship
Who maintains the method.
Artificial Intelligence and Autonomous Systems stewards artificial intelligence and machine learning for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of artificial intelligence and machine learning in this field?
Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.
How are results checked?
Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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Financial Crime and Fraud Risk
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