Cross-sector intelligence sharing is essential. With fraud scam losses projected to hit $62 billion by 2025 and growing annually by 19.3%, your institution faces threats that are often invisible and move faster than traditional detection methods. Criminals operate in coordinated networks. Your response must do the same.
This checklist helps fraud managers assess their readiness to participate in cross-sector intelligence sharing networks and identify the steps needed to join them effectively.
What This Checklist Covers
This checklist outlines the operational, legal, and technical requirements for participating in cross-sector intelligence sharing. It's designed for fraud teams ready to collaborate but haven't yet taken concrete steps. Each item includes the compliance or operational standard it addresses and describes what successful implementation looks like.
Prerequisites
Before using this checklist, ensure you have:
- Executive sponsorship: A VP-level or higher sponsor who understands that intelligence sharing offers operational advantages, not just compliance obligations.
- Legal counsel availability: Access to attorneys who can review data sharing agreements and assess liability protections.
- Technical resource allocation: At least one engineer or analyst to evaluate integration requirements and data formatting standards.
- Clear understanding of your current detection gaps: Document where fraudulent activity enters your system undetected or where you lack threat context.
Readiness Checklist
1. Legal Framework Assessment
Review your jurisdiction's data protection and information sharing laws to determine what types of threat intelligence you can share without customer consent.
Requirement reference: GDPR Article 6(1)(f) (legitimate interests), CCPA Section 1798.140(o)(2) (fraud prevention exception), and equivalent provisions in your operating jurisdictions.
What good looks like: You have a written legal opinion stating which signal types (threat URLs, compromised credentials, fraudulent account indicators) you can share under existing law, and which require additional safeguards or cannot be shared.
2. Data Classification and Sensitivity Mapping
Classify the fraud signals your team collects by sensitivity level: public (threat URLs, known scam domains), low-sensitivity (aggregated typology patterns), medium-sensitivity (de-identified behavioral indicators), high-sensitivity (customer-specific transaction data).
Requirement reference: Your institution's data governance policy and information security classification standards.
What good looks like: You've created a matrix showing signal type, sensitivity level, and sharing eligibility. Your team can start with low-sensitivity signals while building confidence in the framework.
3. Governance Structure Documentation
Define who in your organization can approve intelligence sharing, what approval process applies to different signal types, and how you'll track what's been shared.
Requirement reference: Your AML/CFT Framework governance requirements and BSA compliance program documentation standards.
What good looks like: You have a one-page decision matrix showing signal type, required approver, and documentation requirements. Your compliance officer has reviewed and approved it.
4. Partner Network Evaluation
Research available intelligence sharing platforms and consortiums. Evaluate their accreditation requirements, governance models, liability protections, and participant composition.
Requirement reference: Third-party risk management standards in your vendor management policy.
What good looks like: You've documented at least three sharing networks, compared their trust models, and identified which sectors they connect (financial services, technology platforms, telecoms, government). You know whether they offer Safe Harbor protections or equivalent liability shields.
5. Technical Integration Scoping
Assess what data formats the sharing network accepts, what APIs or data exchange protocols it uses, and how signals will flow into your existing fraud detection systems.
Requirement reference: Your information security architecture standards and API security requirements.
What good looks like: Your technical team has reviewed the integration documentation and provided a time and resource estimate. You know whether this is a one-week integration or a three-month project. You've confirmed the signals are lightweight enough to ingest without rebuilding your detection infrastructure.
6. Reciprocity Planning
Determine what signals your team can contribute back to the network. Remember: one integration with a platform like the Global Signal Exchange gives access to more than 50 data sources, but the model depends on participants contributing what they can.
Requirement reference: None specific, but reciprocity is a trust-building mechanism in most sharing frameworks.
What good looks like: You've identified at least three signal types your team can share (even if you start with just threat URLs) and documented the internal process for extracting and formatting them.
7. Operational Workflow Design
Map how incoming intelligence will reach your fraud analysts, how they'll validate and act on it, and how you'll measure its impact.
Requirement reference: Your fraud operations procedures and performance measurement framework.
What good looks like: You've updated your standard operating procedures to include a section on cross-sector intelligence. Analysts know what to do when they receive a signal about compromised infrastructure or linked fraudulent accounts. You've defined at least two metrics to track effectiveness (such as time-to-detection improvement or false positive rate reduction).
8. Pilot Scope Definition
Start small. Choose one signal type, one sharing partner, and a defined test period (typically 30 to 90 days).
Requirement reference: Your change management and pilot program standards.
What good looks like: You've documented pilot objectives, success criteria, and rollback procedures. Your legal and compliance teams have approved the pilot scope. You're not trying to share everything at once.
Common Mistakes
Waiting for perfect legal clarity: You won't get a statute that explicitly authorizes every type of intelligence sharing. Work within the fraud prevention exceptions that already exist in data protection law.
Treating this as an IT project: Intelligence sharing is an operational capability that requires legal, compliance, and fraud operations working together. If IT leads it alone, you'll build a technical integration that no one uses.
Sharing everything or nothing: The institutions gaining the most operational advantage didn't start by opening their entire fraud database. They started with low-sensitivity signals and expanded as trust built.
Assuming your institution is too small to contribute: Criminal networks exploit infrastructure across institutions of all sizes. The fraudulent domain you blocked yesterday might be targeting three other organizations today. Your signals have value.
Ignoring the force multiplier effect: A small number of shared signals can lead to identification of tens of thousands of fraudulent accounts when organizations can see the connections between shared infrastructure, linked accounts, and common patterns. Don't underestimate the asymmetry that becomes possible when fragments come together.
Next Steps
If you checked off items 1 through 4, you're ready to approach an intelligence sharing network with specific questions. If you completed items 5 through 7, you're ready to join a pilot program. If you've reached item 8, you're already ahead of most institutions.
Criminals are already sharing intelligence. They reuse domains, hosting services, payment rails, and communication channels across networks. Your institution's choice isn't whether to collaborate, but whether to do it fast enough to matter.
Start with one signal type. Choose one trusted partner. Set a 30-day milestone. The infrastructure exists. What's needed now is the commitment to use it.
