Push payment fraud is usually searched when someone wants to understand how the scam works, where the money goes, and what fraud teams can do about it. For banks and payment providers, the hard part is that the customer is persuaded to authorize the payment, which changes both detection and response. This article focuses on payment routes: bank transfers, crypto transfers, gift cards, payment handles, and the contact points that help connect activity to an actor. It gives fraud teams a practical view of how routes are used, what signals they create, and how conversation-derived intelligence from Active Defense can add context early in an investigation, alongside briefings and reports.
What Push Payment Fraud Means in Practice
Push payment fraud is fraud in which the customer is persuaded to send the money themselves. The transfer is authorized by the payer, but the authorization comes from deception, pressure, or a false story rather than a legitimate reason to pay.
That distinction matters to banks and payment providers because the payment can appear valid at the moment it is made. The customer completed the action, yet the intent behind it was shaped by a scammer.
In practice, the scam usually combines two elements:
- a reason to act
- a destination to send value to
The reason may be fear, urgency, trust, or the promise of a gain. The destination may be a bank account, a crypto wallet, a gift card, or a payment handle. What makes this category operationally difficult is that the social engineering and the payment instruction are part of the same event.
Common scenarios include:
- fake security alerts telling the customer to move funds
- invoice or supplier-payment deception
- investment stories
- romance or relationship grooming
- purchase scams
- impersonation of a business, authority, or family member
For fraud teams, push payment fraud is not just a payment problem. It is a persuasion problem that produces payment activity. The key questions are therefore broader than whether the transfer was customer-initiated. Teams need to understand what story was used, what destination was provided, and which contact details supported the request.
That is why these cases should be read as payment instructions delivered through a scam conversation, not as isolated transfers. The money movement matters, but the setup around the payment is often what explains the case.
The Main Payment Routes Scammers Use
The main payment routes in push payment fraud fall into a few practical categories:
- bank details, including IBAN and SWIFT
- crypto wallets on BTC, ETH, TRON, and SOL
- gift card requests
- payment handles
- phone numbers
- email addresses
- messenger accounts
- links that move the victim to the next step
Different scam types tend to favor different routes, but many operations do not rely on just one. A scam may begin with a messenger account or email address, move through a link to a fake onboarding page or payment instruction, and end with bank details or a crypto wallet. Looking at only the final destination can hide how the route was assembled.
Crypto can appeal to scammers because transfers can be fast and cross-border. Bank routes, by contrast, can fit scams that depend on credibility and familiar customer behavior, especially when the payment request is presented as routine, urgent, or businesslike. For a deeper look at what wallet data can reveal to fraud teams, see what scammers’ crypto wallets tell fraud teams.
Gift cards and payment handles can serve a different role. They can offer a simpler collection path or support smaller-value transfers that feel less formal to the victim than a bank transfer. That matters operationally because the payment route may be designed not just for collection, but for persuasion.
In investigations, the route is often more useful as a chain of indicators than as a single endpoint. The contact point, the payment request, the link used to direct the victim, and any files or images used to support the story can all help explain how the route works.
File hashes and photo hashes matter for the same reason. When the same image, document, or supporting file appears across multiple chats, it can help connect activity that would otherwise look unrelated.
What Fraud Teams Need to Collect From the Conversation
In push payment fraud, the payment record rarely holds the full case. What matters in the conversation is the set of instructions, identifiers, and supporting artifacts the scammer uses to move the payer from trust to transfer.
The useful collection set usually includes:
- crypto wallets
- bank details
- phone numbers
- messenger accounts
- links
- gift cards
- payment handles
- file hashes
- photo hashes
Collected together, these items show how the request was delivered, where value was meant to go, and what supporting material was used to make the story credible.
Active Defense gathers these indicators from scammer conversations on Telegram and by e-mail and turns them into a threat-intelligence feed. Each indicator carries a confidence score. For analysts, that helps separate stronger leads from items that need more review before they shape a case decision.
The timing of collection matters as much as the field list. In scam chats, payment instructions, account details, photos, voice messages, and files can appear briefly and then be removed. Every message is stored the moment it arrives, so a scammer’s later “delete for everyone” does not remove the copy. That preserves the original sequence of the exchange and keeps the surrounding evidence attached to the payment request.
Another key step is correlation. Chats that share an indicator, a reused photo, or a forwarded account are linked to one actor. That lets investigators move from one reported destination to a fuller view of how the same actor presents instructions, rotates collection points, and reuses supporting material across conversations.
Replies are drafted by AI and approved by a human operator by default.
How Active Defense Adds Route Intelligence
Active Defense is a system that runs AI decoy personas on Telegram and by email to keep scammers talking and turn what they hand over into a threat-intelligence feed for each customer. In the context of push payment fraud, that matters because route intelligence often appears inside the scammer’s own instructions, follow-up messages, files, and contact details.
Replies are drafted by AI and approved by a human operator by default before anything is sent. That keeps human review in the loop while still helping extend scammer conversations and capture more of the route around a payment request.
For fraud teams, the value is not limited to a single reported destination. The customer-specific feed can enrich investigations with indicators gathered directly from scammer interactions, including the payment endpoints and the surrounding contact points that help explain how the route is being used. That gives teams more context than post-incident reports alone.
Each customer gets their own feed with STIX 2.1 export. Because the feed is customer-specific, teams can work from indicators and linked activity relevant to their own investigations and internal processes.
The console and API run on Azure in Germany.
Teams that want to assess the approach can use the free feed sample for qualified leads and the 90-day pilot for up to 1,000 sessions at a fixed price.
Using Payment-Route Signals in Bank and PSP Operations
In bank and PSP operations, payment-route signals are useful because they add context around a reported destination instead of treating one account, wallet, or handle as the whole case. That can help teams enrich investigations, support triage, connect related reports, and judge whether a destination looks isolated or part of a broader actor pattern.
In practice, teams can use route intelligence to:
- compare reported payment destinations with related contact points and files
- group cases that share indicators or linked chats
- prioritize review when many reports arrive at once
- decide which cases may justify deeper investigation first
This also helps different internal teams work from the same picture. Fraud operations, investigations, and intelligence teams can review a shared set of indicators and linked chats rather than passing along only a single payment endpoint. That makes it easier to discuss whether the case reflects one attempted collection point, a reused route, or activity tied to a wider network, including patterns relevant to mule account investigations.
Scored indicators matter here because investigative time is limited. When several leads come in together, a confidence score can help analysts decide what to examine first. Linked actor views add another layer: reports that appear separate at intake may still point to the same collection network or the same scammer persona.
Questions Fraud Teams Ask
What makes push payment fraud harder to detect than card or account-takeover fraud?
The customer completes the payment themselves, so many controls see a valid instruction, not a blocked one. That shifts the review from pure transaction anomaly to decision context.
For fraud teams, the practical issue is whether the payment was shaped by deception. That means asking what contact channel was used, what story created urgency, and what payment method the scammer pushed the customer toward.
Where do fraud teams usually lose visibility in a push payment case?
Visibility often breaks between the scam conversation and the payment event. The transfer may be logged clearly, while the surrounding instructions sit in screenshots, inboxes, chat exports, or deleted messages.
The missing pieces often include:
- the first contact point
- the payment instructions themselves
- follow-up changes to destination details
- supporting files, photos, or voice messages
When those pieces stay separate, teams can miss connections between cases that use different destinations but the same actor.
How can conversation-derived intelligence help before a case is fully built?
It can give analysts usable context earlier, while the picture is still incomplete. Active Defense runs AI decoy personas on Telegram and by e-mail to keep scammers talking and turn what they hand over into a threat-intelligence feed.
The feed can include crypto wallets, bank details, phone numbers, messenger accounts, links, gift cards, payment handles, and file and photo hashes, each with a confidence score. Chats that share an indicator, a reused photo, or a forwarded account are linked to one actor.
Each customer gets their own feed with STIX 2.1 export.
Conclusion
Push payment fraud is best handled as an instruction chain, not just a transfer. For banks and payment providers, that means keeping the payment endpoint tied to the contact details, files, and route changes that shaped the customer’s decision.
Useful case inputs include:
- scored indicators from scammer conversations
- links between chats that point to one actor
- a customer-specific feed with STIX 2.1 export
Active Defense supports that on Telegram and by e-mail, with replies drafted by AI and approved by a human operator by default.