Detect card-based money laundering at the PAN level.
AML Account Risk augments issuer AML processes by identifying potential money laundering on card accounts. It uses Mastercard network-wide transaction intelligence, AI-driven profiling and aggregated PAN-level scoring to surface high-risk activity that traditional rule-based monitoring can miss.
- High-risk laundering parameters
- 14
- Aggregated risk scoring
- PAN-level
- Transaction monitoring
- Near real-time
- Alert generation
- AI-driven
High-risk laundering parameters
Aggregated risk scoring
Transaction monitoring
Alert generation
Why organisations use AML Account Risk
Card-based money laundering is estimated at roughly $500 billion globally, yet many issuer AML programmes rely on rules that were not designed for the velocity and scale of card transactions. AML Account Risk monitors approved Mastercard authorisation transactions across brands, segments, products and channels, then profiles them using supervised and unsupervised machine learning across 14 high-risk parameters. The result is an aggregated risk score at the PAN level for every transaction, incorporating daily, weekly and monthly activity, so investigators can focus on the accounts that really matter. Notifications are enriched with contextualised transaction data, issuers can submit feedback through the API to improve model accuracy, and a full feedback history supports audit and compliance reviews.
Network-wide transaction monitoring
Near real-time monitoring of approved Mastercard authorisation transactions across brands, segments, products and channels via the Mastercard Network.
AI-driven laundering detection
Multiple machine-learning models identify money laundering patterns across 14 high-risk parameters for improved accuracy over static rules.
PAN-level aggregated scoring
Every transaction receives an aggregated risk score at the PAN level, combining daily, weekly and monthly activity.
Threshold-based flagging
Potentially suspicious PANs are flagged automatically when their risk score exceeds predefined thresholds set by the issuer.
Enriched notifications
High-risk PAN alerts include contextualised transaction data so teams can assess and act with greater confidence.
Feedback and continuous learning
Submit feedback directly through the API and review a searchable history of decisions to improve detection accuracy over time.
Retrospective analysis
Specify date ranges to pinpoint high-risk PAN activity for incident response and historical reviews.
Audit-ready feedback history
A searchable trail of submitted feedback supports compliance reviews and tracks decision-making patterns across cases.
Coverage and specifications
Core capabilities
- PAN-level risk scoring
- 14 high-risk laundering parameters
- Network-wide transaction monitoring
- Threshold-based alert flagging
- Enriched notification details
Investigation support
- Date range availability
- Contextual transaction data
- API feedback submission
- Searchable feedback history
- Compliance audit trail
Integration options
- Direct API integration
- ComplianceSuite orchestration
- Alert ingestion into case management
- Correlation with KYC/KYB data
- Risk-based prioritisation workflows
Delivery
- Mastercard AML Account Risk API
- Infocredit Group onboarding and support
- ComplianceSuite integration
- Local training and enablement
- Ongoing analyst support
How it works
- 01
Monitor
Near real-time monitoring of approved Mastercard transactions across brands, segments, products and channels through the Mastercard Network.
- 02
Profile
Supervised and unsupervised machine learning models identify money laundering patterns across 14 high-risk parameters.
- 03
Score
Aggregated scoring at the PAN level for every transaction, incorporating daily, weekly and monthly activity.
- 04
Flag
Potentially suspicious PANs are flagged when their risk score exceeds predefined issuer thresholds.
- 05
Retrieve
Issuers retrieve full details of high-risk PAN notifications for investigation, action and feedback.
Where it is used
- Issuers strengthening AML detection on card accounts with AI-driven, network-level intelligence
- AML teams reducing false positives by focusing on PAN-level aggregated risk rather than isolated transactions
- Compliance teams investigating historical card-based laundering activity through date-range queries
- Investigation teams enriching alerts with contextual transaction data and feedback loops
- Regulated firms documenting AML decision-making with a searchable feedback and audit trail
Outcomes teams report
- Higher-quality AML alerts from network-wide, AI-driven transaction profiling
- Faster triage by prioritising high-risk PANs with aggregated risk scores
- Reduced exposure to AML, sanctions and KYC compliance penalties
- Stronger brand protection through improved detection and regulatory adherence
- Continuous model improvement via investigator feedback through the API
Frequently asked questions
What does AML Account Risk detect?
It detects potential money laundering on card accounts at the PAN level by monitoring approved Mastercard transactions and profiling them across 14 high-risk parameters using machine learning.
How is the risk score calculated?
Every transaction receives an aggregated risk score at the PAN level, incorporating daily, weekly and monthly activity patterns across multiple AI models.
Can alerts be investigated retrospectively?
Yes. Date range availability lets teams pinpoint high-risk PAN activity over a specified period, ideal for incident response and historical analysis.
Does the system improve from feedback?
Yes. Issuers can submit feedback through the API, and a searchable feedback history supports compliance reviews while helping the model improve over time.
How is it delivered by Infocredit?
We help issuers integrate the AML Account Risk API directly or through ComplianceSuite, with local onboarding, training, configuration and ongoing support across our markets.
Interested in AML Account Risk?
One contract, local implementation and support — Securing Ease of Mind.