How payment threat intelligence helps banks fight fraud faster

• Fraudsters are using advanced tactics like deepfakes, infostealers and AI-driven bots to increase the speed and scale of their attacks.  
• Payment threat intelligence can show early indicators of fraud, enabling fraud teams to anticipate and mitigate risks before they escalate. 
• When fraud and cybersecurity teams proactively share threat intelligence data, they can speed up payment fraud detection and prevention. 

Today’s fraudsters are faster, more equipped and more connected. And they’re using AI and automation to exploit vulnerabilities at lightning speed.

In the current climate, the turnaround from data breach to monetization is often less than a day. Nearly 2 out of 3 (65%) compromised credentials are listed for sale less than 24 hours after being stolen. 

Fighting back requires fraud and cybersecurity teams to match the speed of attackers. But without timely threat intelligence, they’re often a step behind. In fact, just 36% of global leaders detect fraud patterns in the period before being notified of a data breach. That’s a worrisome lag, especially since organizations take an average of 241 days to identify and contain a breach. 

To close the gap and accelerate payment fraud detection and prevention, fraud teams need access to payment threat intelligence that surfaces early fraud signals and enables proactive planning. 

Fraudsters are leveraging automation and new digital tools to fast-track the path from cyber breach to financial fraud. These tactics allow them to automate, adapt and scale cyberattacks at unprecedented speed, reshaping how payment fraud begins.

Cybercriminals now deploy AI agents that can autonomously refine attacks in real time. 

For example, fraudsters often use automated bots to test stolen usernames and passwords across multiple sites in credential stuffing attacks. But increasingly, attackers are experimenting with AI tools that can identify more promising credential combinations or target accounts that appear easier to compromise. 

Compared to traditional automated bots that perform simple, repetitive actions, autonomous AI systems are faster and more efficient. They represent the next frontier of cyber threats: adaptive, continuous and increasingly difficult to defend against. 

Not surprisingly, 80% of CISOs now say AI-powered cyber attacks are their top concern. With this evolution, effective fraud prevention depends on identifying early attack indicators to adjust cybersecurity controls and plan proactive response measures.

Infostealers are malware designed to break into systems and capture sensitive personally identifiable information (PII). They fuel payment fraud schemes by automating credential theft, helping attackers harvest large volumes of data far faster than with manual tactics.

Infostealers are also inexpensive and widely available. Amateur criminals can purchase ready-made malware toolkits from online cybercrime-as-a-service marketplaces, making it easy to launch attacks.

After harvesting PII through infostealers, fraudsters can use the data to launch account takeovers and identity fraud, or they can sell it on dark web marketplaces. PII elements vary widely, from Social Security numbers to seemingly minor personal details that can be used for verification bypass. In 2024 alone, 4 million mothers' maiden names were offered for sale alongside stolen card data due to their prevalence in account recovery questions.

Generative AI is accelerating identity fraud by making it faster and easier for criminals to fabricate convincing personas at scale.

Using AI, criminals can rapidly create deepfakes that mimic a real person’s face or voice, allowing them to bypass identity verification and authorize fraudulent transactions. Likewise, synthetic identities — which combine real elements like a Social Security number with an AI-generated name or headshot — are increasingly used to impersonate legitimate applicants in processes like credit card applications or new account openings.

With an abundance of stolen PII now circulating online, cybercriminals can easily assemble fraudulent digital personas to run multiple scams simultaneously. Nearly half (46%) of financial institutions report an increase in deepfake-related fraud attempts, indicating that these attacks are moving from an emerging threat to a mainstream risk.

Payments-specific threat intelligence gives fraud teams visibility into upstream indicators of potential fraud. As fraudsters use AI and other advanced tools to increase the speed and scale of their attacks, this data allows teams to coordinate more effective responses by enabling the following capabilities: 

Silos between cybersecurity and fraud teams continue to hinder coordinated payment fraud detection and prevention efforts. In fact, 3 in 4 executives at global financial institutions say they have a cyber threat intelligence solution but lack the integration capabilities to meaningfully improve their fraud prevention efforts.  

Payment threat intelligence closes this divide by translating cybersecurity data into practical insights that fraud teams can interpret and discuss with their cybersecurity counterparts. 

Payments-specific threat intelligence unites cybersecurity and fraud data in a shared context, giving fraud teams a connected view of risk. When teams share the same playbook, they can develop shared response plans and respond in sync as new threats arise.

For example, integrated insights can help fraud teams understand how a cyber event might lead to downstream fraud activity and plan accordingly. If intelligence shows that criminals are validating stolen card numbers through small test transactions (card testing), the fraud team can take preventative action before losses occur. 

Instead of reacting to confirmed fraud, teams can now get ahead of it. Payment threat intelligence offers early warning signals that enable fraud teams to intervene before criminals can profit.

With Mastercard Threat Intelligence (MTI), those interventions are built in. For example, when MTI detects card testing, it can alert the card issuer and decline testing transactions, even before those cards are used for a high-value transaction that traditional fraud systems might flag.

With this visibility, fraud teams can shift from solely addressing confirmed incidents to anticipating threats and planning defenses, helping prevent losses.

​​​As cyber-enabled fraud accelerates, collaboration between fraud and cybersecurity teams is non-negotiable. Payments-specific threat intelligence is the path forward, helping eliminate silos and drive unified efforts to stop fraud before it starts.

With Mastercard Threat Intelligence, teams can stay ahead of payment fraud threats. Curated insights on emerging risks, automated detection and on-behalf response support a faster, more proactive and coordinated defense to protect customers.

Looking to advance your fraud prevention strategy? ​​Learn how Mastercard Threat Intelligence can help.