Fraud scam losses are projected to reach $62 billion by 2025, growing at 19.3% annually. Your institution sees the money move, but the scam started hours earlier on a platform you don't monitor, through a phone number you can't trace, via a website you'll never discover. By the time the transaction hits your system, the victim is already compromised.
Cross-sector intelligence sharing isn't optional anymore. It's essential infrastructure. Here's how to participate.
The Problem: Why This Matters Now
Criminal networks reuse infrastructure at scale. The same hosting service, domain registrar, and payment rail appear across hundreds of scams. You're detecting fragments while criminals operate across the full attack chain.
Your current detection stack processes signals from your own transaction data, maybe a few purchased feeds, and whatever your fraud analysts manually research. Meanwhile, platforms see the initial contact, telecoms carry the social engineering calls, hosting companies serve the phishing sites, and other financial institutions process related transfers. None of you see the complete picture.
The gap between what you detect and what exists isn't a technology problem. It's a coordination problem. Intelligence sharing platforms like the Global Signal Exchange provide access to more than 50 data sources through one integration, but most institutions haven't connected yet because they don't know where to start.
What You Need Before Starting
Legal Clearance on Outbound Sharing
Work with your legal team now to establish what you can share under existing regulations. Start with low-sensitivity signals: confirmed fraud URLs, known scam phone numbers, verified malicious domains. These typically don't trigger customer privacy concerns because they describe attacker infrastructure, not customer behavior.
Document your legal position before you approach a sharing platform. Know your constraints.
Executive Sponsorship
This isn't a fraud team project. Cross-sector sharing requires sign-off from compliance, legal, information security, and operations. Get a senior leader who can align these functions and make decisions when they conflict.
Technical Integration Capacity
You'll need API integration work and the ability to ingest signals into your existing detection systems. Budget 40-80 hours of engineering time for initial integration, plus ongoing maintenance. If your fraud platform supports webhook ingestion or has a signals API, integration is faster.
A Defined Use Case
Don't start with "we want to share everything with everyone." Pick one specific threat type where you know you're blind. Common starting points: romance scam URLs, cryptocurrency wallet addresses tied to fraud, phone numbers used in authorized push payment scams.
Step-by-Step Implementation
Week 1: Select Your Platform and Apply for Accreditation
Research available intelligence sharing platforms. The GSE operates as an independent clearinghouse with governance rules and accreditation requirements. Other options include regional Financial Intelligence Units (FIUs) that facilitate cross-border Suspicious Activity Report (SAR) sharing, or sector-specific Information Sharing and Analysis Centers (ISACs).
Submit your accreditation application. Expect to provide documentation of your institution's regulatory status, data handling practices, and intended use cases.
Week 2: Configure Your Sharing Controls
Most platforms let you control what you share and with whom. Set conservative defaults initially:
- Share threat indicators (URLs, domains, phone numbers) but not transaction patterns
- Limit sharing to financial sector participants initially
- Require reciprocal contribution from recipients
- Set data retention limits on what you send
You can expand permissions later as you build confidence in the framework.
Weeks 3-4: Build Your Integration
Standard integration pattern:
- Implement the platform's API authentication (typically OAuth 2.0 or API key)
- Configure webhook endpoints to receive real-time signals
- Map inbound signals to your fraud detection system's data model
- Set up automated enrichment: when you receive a malicious URL, automatically check if it appears in your transaction history or customer-reported phishing attempts
Test with a small signal set before going live. Verify that signals flow correctly and trigger appropriate responses in your detection rules.
Week 4: Submit Your First Signals
Start with confirmed fraud cases from the past 90 days. Extract infrastructure indicators:
- Domains and URLs from phishing attempts
- Phone numbers from social engineering attacks
- Cryptocurrency addresses from investment scams
- Email addresses used in business email compromise
Format them according to the platform's schema (usually STIX 2.1 or a similar structured threat intelligence format) and submit in batches. Don't wait for perfection; submit what you have and refine your process.
Validation: How to Verify It Works
Immediate Validation
Within 48 hours of going live, you should see inbound signals arriving. Check:
- Are signals reaching your fraud platform's queue?
- Do they match the threat categories you subscribed to?
- Can you trace at least one inbound signal to a decision point in your detection logic?
30-Day Operational Test
Track these metrics for your first month:
- Number of unique signals received vs. number you submitted (healthy sharing platforms show 10:1 or higher ratios)
- Percentage of inbound signals that matched entities in your transaction data (even 1-2% is valuable; it means you're catching threats you'd have missed)
- Time from signal receipt to detection rule update (target: under 4 hours for critical threats)
Force Multiplier Check
The real test: did a small number of signals you contributed lead to broader network discoveries? One institution shared a handful of romance scam URLs through the GSE, which led to identification of tens of thousands of fraudulent accounts and thousands of cloned websites because other participants could see the shared infrastructure patterns.
If you're only receiving signals and never seeing network effects from what you share, you're not contributing enough to benefit from the asymmetry that makes cross-sector sharing powerful.
Maintenance: Ongoing Tasks
Weekly: Review Signal Quality
Check for false positives in signals you've received and signals you've sent. High false positive rates (above 5%) indicate you need to tighten your submission criteria or challenge the source.
Monthly: Expand Your Contribution Scope
As you build confidence, gradually increase what you share:
- Add new threat categories
- Extend sharing to non-financial sector participants (platforms, telecoms)
- Contribute behavioral patterns in addition to infrastructure indicators
Quarterly: Governance Review
Meet with legal, compliance, and information security to review:
- Any incidents or concerns related to shared data
- Changes in regulatory guidance that affect what you can share
- Opportunities to deepen participation
Ongoing: Operationalize Signals in Detection Rules
Intelligence sharing fails when signals arrive but don't change decisions. Ensure your fraud analysts and detection engineers regularly incorporate shared signals into:
- Real-time transaction scoring models
- Customer communication review queues
- Proactive account monitoring triggers
The institutions gaining operational advantage from cross-sector sharing aren't just receiving signals. They're using them to detect threats before money moves, not after.
Criminal networks already operate across sectors. Your detection infrastructure should too.
