Fraud runs as a network.
Defense should too.
Fraudsters operate across dozens of banks at once. Vyntra’s Community Scoring & Intelligence (CS&I) brings network and community risk signals into your real-time risk scoring, so you stop what institution-level detection alone cannot see.
Lost to fraud globally in 2025
US$
0
B
INTERPOL, 2026
APP fraud losses, UK alone, 2024
£
0
M
UK Finance, 2025
Estimated annual APP fraud losses across EU
€
0
B
Source: EDC (Edgar, Dunn & Company)
Fraud detection uplift with community intelligence
+
0
%
Source: Vyntra data
The challenge
Institution-level detection has a structural blind spot.
Criminals organize at network scale, forming fraud rings that operate across borders. Mule accounts are layered across banks. Scam campaigns, increasingly using deepfakes and coordinated social engineering, run across PSPs simultaneously. Your models profile your customers — but a beneficiary that looks clean to you may already be flagged at three other institutions.
The signals that would expose them exist. They just sit scattered across institutions, invisible to any single bank’s models.
Even when the threat intelligence is available — scheme APIs, community feeds, CERT indicators — plugging it into a live scoring pipeline takes know-how, data-science and infrastructure that most teams would rather not spend in-house. The signals are out there. The build is what stops most institutions acting on them.
Single-bank visibility
A beneficiary that looks new and clean to your institution may already be a flagged mule at four others. You cannot see what you are not shown.
No community to belong to
Most institutions handle fraud prevention in isolation. Criminals already operate as a network. Defenders mostly do not. Each bank relearns the same threats alone.
Hard-to-share signals
Privacy, competition and legal constraints make raw data sharing impractical. Useful intelligence exists — but rarely reaches the real-time decision.
How we deliver it
Network-aware scoring. Off the shelf.
Community signals folded into the score as weighted Artificial Intelligence (AI) and machine learning features — not blunt blacklists. No new data plumbing. No in-house build.
Network signals at the score
Brings payment-scheme and network-level risk data into the payment score, so risk seen across the wider network helps protect your customers and strengthen your payment fraud defenses, without you having to integrate it yourself.
Automated community feeds
Ingests and maintains indicator lists and fraud signals from regional CERTs and MISP-type communities automatically — as weighted features, not blocklists. Easier to manage. More effective in anomaly detection.
Community hub membership
Connects your institution to a trusted circle for sharing entities, alerts and signals on fraudulent activity in both directions. Each member benefits from what the others see — and strengthens the network in return.
How it works
It takes a network to defeat a network.
From the community
Institutions already fighting fraud together.
Community signals folded into the score as weighted AI features — not blunt blacklists. No new data plumbing. No in-house build.
"
Together we try to find new ways to mitigate these new frauds and improve our risk-models. It is important for us that other community members can benefit in a trustworthy environment from frauds which we have encountered.
Romano Ramanti
Ethical Hacker, Zürcher Kantonalbank
"
The Community Scoring & Intelligence Service will help organizations better protect themselves from cybercrime and fraud. We are pleased to be involved in this global movement to stop the fraudsters and help financial institutions protect their customers.
Michael Fuchs
Senior Information & Cyber Security Consultant, SWITCH-CERT
"
Banque Cantonale de Fribourg significantly benefits from the Community Scoring & Intelligence service. The ability to automatically integrate, capture and share insights allows us to flag fraudulent counter-parties already identified by the community. We believe sharing intelligence is the way to combat fraud, and highly recommend this service.
Anne Maillard
Board Member, Banque Cantonale de Fribourg
Proof points
What collective intelligence delivers.
Detection uplift when community intelligence is added to institution-level AI scoring
+
0
%
Source: Vyntra data
Recovered by Singapore's Anti-Scam Command and partners in 2025 — coordination works
US$
0
M
Source: Singapore Police Force
nnual fraud estimated preventable by Verification of Payee across the euro area
€
0
B
Source: EBA CLEARING
APP fraud losses, UK alone, 2024
£
0
M
Vyntra is one of only five official FPAD solution providers recognized by EBA CLEARING
Who this speaks to
Built for teams sensitive to systemic risk.
Community signals folded into the score as weighted AI features — not blunt blacklists. No new data plumbing. No in-house build.
Head of Fraud / Financial Crime
Accountable for detecting scams and money laundering networks that operate across institutions.
- Catch cross-bank mule and scam patterns, including synthetic identity fraud, that their own models cannot see
- Raise detection without raising false positives
- Avoid in-house build cost for integrating external signals
Head of Payments Operations
Owns real-time payment execution and SLAs, including instant payments.
- Add network checks and transaction monitoring without breaching the instant-payment time window
- Minimal operational disruption and integration effort
- Keep legitimate payments flowing at volume
Chief Risk Officer
Oversees fraud exposure, anti-money laundering (AML) obligations, and the institution’s regulatory posture.
- Demonstrate network-level controls to regulators
- Quantify residual exposure from institution-only detection
- Protect trust and reputation on the network
GET IN TOUCH
Stop fighting fraud alone.
See how Vyntra’s Community Scoring & Intelligence (CS&I) solution brings network and community risk signals into your real-time scoring — delivering stronger detection, fewer false positives, and a seat in a community of institutions that exchange intelligence.
FAQs
What is a fraud intelligence platform?
A fraud intelligence platform brings together risk signals from multiple sources and applies them to fraud detection and payment decisioning. These may include payment-scheme data, community intelligence, CERT indicators and signals shared by other financial institutions.
The goal is to identify risks that may be invisible when an institution relies only on its own customer and transaction data.
Why is institution-level fraud detection not always enough?
Institution-level models can only learn from the activity visible within one bank or payment provider. Fraud networks operate across multiple institutions, so a beneficiary that appears new or low risk to one bank may already have been linked to mule activity, scams or other suspicious behavior elsewhere.
Vyntra’s community and network intelligence helps expose these cross-institution patterns before the payment is approved.
How are community fraud signals used in payment scoring?
Community signals are incorporated into the payment score as weighted risk features rather than treated as automatic blocklist matches.
This means they can be assessed alongside transaction, behavioral and customer risk data, giving fraud teams a fuller view without automatically stopping every payment connected to an external indicator.
What types of fraud can community intelligence help detect?
Community intelligence is especially useful for fraud that spans multiple institutions, including mule-account networks, APP scams, fraud rings, account takeover, synthetic identity fraud, multi-accounting and coordinated social-engineering campaigns.
Its value is greatest where activity looks legitimate in isolation but becomes suspicious when viewed across the wider network.
Does community intelligence require institutions to share raw customer data?
No. Institutions do not need to share unrestricted customer or KYC data to benefit from collective intelligence.
Relevant entities, indicators, alerts and risk signals can be exchanged within a controlled framework, allowing useful intelligence to inform scoring without exposing unnecessary personal or commercially sensitive information.
Can external threat-intelligence feeds be added to real-time payment decisions?
Yes. Payment-scheme signals, CERT indicators and MISP-type community feeds can be ingested, maintained and applied within the real-time scoring process.
Vyntra handles this as part of the service, reducing the need for fraud teams to build and manage separate integrations for every external source.
Will adding network signals increase false positives?
Not necessarily. Network and community signals work best as part of the wider risk score, not as standalone reasons to decline a payment.
By weighing them alongside behavioral and institution-level data, fraud teams can improve detection while continuing to distinguish legitimate payments from genuinely suspicious activity.
Can network intelligence be used for instant payments?
Yes. Network and community signals can be applied within a live payment decision without creating a separate manual review step for every transaction.
This allows institutions to strengthen fraud controls while still meeting the speed requirements of instant payment systems.
Who benefits most from community-based fraud intelligence?
It is most relevant for banks, payment service providers and other financial institutions that need to detect fraud spanning multiple organizations.
Typical users include heads of fraud, financial crime teams, payments operations leaders, cybersecurity teams and chief risk officers responsible for reducing fraud exposure without disrupting legitimate payment volume.
