Reported impact
Business outcome
Mastercard reports stronger fraud detection and materially fewer false positives, helping banks protect cardholders and improve approval accuracy in real time.
- Customer Experience
- Risk Reduction
In-houseFinancial ServicesFraud DetectionRisk ManagementAI and Machine LearningGenerative AI / LLMCustomer ExperienceRisk Reduction2024 In-houseIN#0f766eFinancial ServicesglobalMastercard needed to improve fraud detection for banks and cardholders as fraudsters increasingly exploit technology, while also reducing false positives that incorrectly flag legitimate transactions.143 billion transactions a yearFeb 1, 20246Fraud Detection|Risk ManagementDecision Intelligence · Decision Intelligence Pro0 2024-02-01 6
Mastercard
Fraud Detection · Risk Management
Mastercard needed to improve fraud detection for banks and cardholders as fraudsters increasingly exploit technology, while also reducing false positives that incorrectly flag legitimate transactions.
143 billion transactions a year
In less than 50 milliseconds
boost fraud detection rates on average by 20% and as high as 300% in some instances
one trillion data points
Solution
Mastercard is extending its existing Decision Intelligence (DI) fraud platform with Decision Intelligence Pro, a generative AI enhancement built into its real-time transaction decisioning flow. The implementation starts with live payment signals already analyzed by DI, including account, purchase, merchant, and device information. Decision Intelligence Pro then evaluates the relationships among multiple entities surrounding each transaction and scans one trillion data points to predict whether the payment is genuine or fraudulent. The resulting risk assessment sharpens the DI score that Mastercard returns to banks in less than 50 milliseconds, so issuers can make safer approval decisions in real time. The release does not name a separate storage or orchestration product, but it clearly describes a transaction-scale AI scoring layer embedded in Mastercard's production payments network and delivered directly into bank fraud controls.
Data flow
For each payment transaction, Mastercard analyzes account, purchase, merchant, and device signals, then Decision Intelligence Pro evaluates relationships among the surrounding entities against one trillion data points. Its output sharpens the Decision Intelligence score, which is returned to banks in less than 50 milliseconds to help approve genuine payments, block suspected fraud, and reduce false positives.
Solution architecture
- Compute
- Decision Intelligence Pro The generative AI enhancement that assesses relationships between entities around a transaction to improve fraud risk scoring in real time.
- Serving
- Decision Intelligence Mastercard's real-time decisioning solution that scores payment transactions and provides risk data to banks for approval decisions.
Architecture clues
- Decision Intelligence Pro was announced as becoming available later in 2024
- Entity-relationship analysis is used to determine transaction risk
- Real-time decisioning flow embedded in Mastercard's payments network
- The scoring system operates at a scale of 143 billion transactions a year
Evidence from the source
"In less than 50 milliseconds, this technology improves the overall DI score, sharpening the data provided to banks."
"Initial modelling shows AI enhancements boost fraud detection rates on average by 20% and as high as 300% in some instances."
"Mastercard is adopting generative AI techniques to enhance the protections that keep consumers – and the entire payments network – safe."
"Mastercard's world-leading Decision Intelligence (DI) - a real-time decisioning solution - already helps banks score and safely approve 143 billion transactions a year."
"New generative AI technology will scan an unprecedented one trillion data points to predict whether a transaction is likely to be genuine or not, building Mastercard's existing ability to analyze account, purchase, merchant and device information in real time."
"The next generation technology, Decision Intelligence Pro, works by assessing the relationships between multiple entities surrounding a transaction to determine its risk."
"The precision of the solution – achieved by scanning potential points of sale in real time - has been shown in our own analysis to not only increase accuracy, but also reduce the number of false positives by more than 85%."
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value100/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact25/253 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
- Source authority25/25The deployment is described by the adopting organization or a government source.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/207 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.mastercard.com/car/en/news-and-trends/press/2024/february/mastercard-supercharges-consumer-protection-with-gen-ai.htmlMastercardFirst-party
Mastercard customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page Feb 1, 2024 · Mastercard · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
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