In-house Financial Services global Company newsroom

Mastercard

Fraud Detection

Fraudsters steal payment card numbers through spyware, malware, card skimming, and other clandestine methods, then post partial card numbers on illegal websites, making it difficult to identify compromised cards quickly enough to block fraud.

doubling the detection rate of compromised cards
increasing the speed of identifying merchants at-risk from - or compromised by - fraudsters by 300%
reducing false positives during the detection of fraudulent transactions against potentially compromised cards by up to 200%

Reported impact

Business outcome

Mastercard says the generative AI enhancement lets it detect compromised cards faster and more accurately, helps identify at-risk merchants sooner, and enables banks to block and reissue cards more quickly to reduce fraud.

  • Customer Experience
  • Risk Reduction
  • Scalability

Solution

Mastercard implemented a generative AI-based predictive fraud detection capability within its card security operations to identify compromised payment cards and suspicious merchant exposure faster across its network. The system scans transaction data spanning billions of cards and millions of merchants to detect complex fraud patterns and predict the full details of cards that criminals partially expose on illegal websites. Mastercard says this enhancement strengthens its existing Cyber Secure security suite, which has been available since 2020 and maintains transparent cybersecurity information on bank and merchant online profiles, including suspected compromised cards. In practice, the AI-driven detection layer analyzes large-scale network activity, flags likely compromises, and feeds higher-speed, higher-accuracy alerts to banks so they can block and reissue cards and monitor attempted transactions to mitigate fraud.

Data flow

Transaction data across billions of cards and millions of merchants is scanned to surface complex fraud patterns and predict full compromised card details from partial exposures; the resulting risk signals enhance Cyber Secure and trigger faster, more accurate alerts to banks, which can then block, reissue, and monitor affected cards.

Solution architecture

1 components · 1 layer
  1. Governance
    • Cyber Secure Provides Mastercard's security suite and baseline cybersecurity profiles for banks and merchants, including suspected compromised card information, which the generative AI enhancement strengthens.

Architecture clues

  • Alerts banks when a card is likely to have been compromised so the card can be blocked and reissued
  • Cyber Secure has been available since 2020
  • Cyber Secure maintains cybersecurity information on bank and merchant online profiles
  • Predicts full card details from partially exposed 16-digit card numbers posted on illegal websites
  • Scans transaction data across billions of cards and millions of merchants

Evidence from the source

"Cyber Secure, available since 2020, uses integrated technology to create a baseline of transparent cybersecurity information on bank and merchant online profiles in the payment ecosystem, including details of suspected compromised cards."
"Mastercard is using generative AI to double the speed at which it can detect potentially compromised cards"
"The new technology works by scanning transaction data across billions of cards and millions of merchants at faster rates than previously imaginable."
"Using generative AI-based predictive technology built by Mastercard it is able to protect future transactions against emerging threats"
"doubling the detection rate of compromised cards"
"increasing the speed of identifying merchants at-risk from - or compromised by - fraudsters by 300%."
"reducing false positives during the detection of fraudulent transactions against potentially compromised cards by up to 200%"
Transparency Evidence & methodology 100/100 reference value · 94% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

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 clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    7 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Mastercard

    Mastercard customer story

    First-party
    https://www.mastercard.com/car/en/news-and-trends/press/2024/may/mastercard-accelerates-card-fraud-detection-with-generative-ai-technology.html
    Source type
    Customer-published story
    Published
    May 22, 2024
    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 May 22, 2024 · Mastercard · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.