Initialize Your Cyber-Fraud Visibility Diagnostic

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What is the approximate annual revenue of the organization you protect?

A. Under $50M

B. $50M - $500M

C. $500M - $1B+

D. PE / VC Backed Portfolio

Which of the following best describes your current position or responsibility?

A. Strategic Leader: (CISO, VP of Fraud/Risk, CRO) – I own the budget and strategy.

B. Operational Leader: (Director/Head of SOC or Fraud Ops) – I manage the team and execute strategy.

C. Practitioner: (Analyst, Engineer, Investigator) – I do the work.

D. Consultant/Advisor: I advise companies on risk.

What is the operational scale of the cyber-fraud/risk function you currently lead or support?

A. Enterprise / Multi-Layered: (15+ specialists, specialized units)

B. Mid-Market / Scaling: (3–14 specialists, dedicated team)

C. Lean / Tactical: (1–2 specialists, owner-operator)

D. Non-Operational: (Student, Researcher, Other)

What is the primary driver for assessing your visibility gaps today?

A. Recent Incident or Near-Miss: We took a hit or saw a scare; I need to ensure it doesn't happen again.

B. Strategic Review / Transformation: We are launching new products or digitizing, and I need to de-risk the growth.

C. Audit or Board Pressure: I need to provide assurance and quantified risk metrics to leadership.

D. Proactive Learning: Just benchmarking our capabilities against the market.

How clearly defined and consistently upheld is ownership (RACI) for cyber-fraud and synthetic identity risk across your organization?

A. Ownership is fragmented or unclear; responsibilities shift depending on the incident.

B. Named owners exist in key functions, but RACI is informal and overlaps or conflicts in practice.

C. Clear primary ownership with documented RACI exists and is usually followed, though not always reviewed.

D. A single accountable owner with a formal, current RACI across fraud, risk, cyber, AML/KYC, and business lines is embedded, monitored, and regularly reviewed.

How consistently do you integrate signals across the full fraud lifecycle—from CIP/KYC and onboarding through behavior, device and network intelligence (e.g., device fingerprinting, IP reputation), transaction monitoring, and collections/exit?

A. Signals are mostly siloed within individual tools or teams, including cyber and fraud.

B. Some signals are shared ad hoc for major cases or escalations, but not by design.

C. We have regular, structured sharing of key signals across multiple stages, including some device/network intelligence.

D. We operate with a deliberate end-to-end signal strategy that links onboarding (CIP/KYC), device/network data, behavior, and lifecycle events into a unified view used for both detection and governance.

When a high-risk anomaly emerges (e.g., suspected synthetic cluster, coordinated bust-out), how consistently does your organization move from detection to a clear decision (contain, block, accept, or escalate)?

A. Response is largely reactive; timelines depend on who notices and who is available.

B. We have some playbooks, but they’re inconsistently followed across teams or time zones.

D. We operate with time-bound playbooks, cross-functional “war room” activation, and monitored SLAs, with lessons learned feeding back into process and controls.

How consistently do fraud, cyber, AML/KYC, and credit risk teams share context on emerging patterns like synthetic identities or cyber-enabled fraud?

A. Collaboration is case-by-case and mostly driven by personal relationships.

B. There are occasional cross-team reviews, usually after significant incidents or audits.

C. We have scheduled cross-functional reviews for patterns and emerging threats, with some data sharing.

D. We run structured, recurring threat reviews with shared data, jointly agreed scenarios, and documented actions across fraud, cyber, AML/KYC, and credit/business.

How consistently do you provide executive leadership and the Board with clear, quantified reporting on synthetic identity and cyber-enabled fraud exposure, aligned to risk appetite and regulatory expectations—not just generic “fraud losses”?

A. Reporting on synthetics and cyber-fraud is minimal or folded into broad fraud/credit loss categories.

B. Executives receive periodic updates, but synthetic/cyber patterns and exposure are only partially visible.

C. We provide recurring reports with some segmentation of synthetic or cyber-enabled exposure, loosely tied to risk appetite.

D. We provide structured dashboards and narratives on synthetic and cyber-fraud trends, scenarios, and control effectiveness, explicitly linked to risk appetite, tolerances, and regulatory themes.

Which statement best reflects how your organization balances fraud tooling, documented processes, and investigator or adversarial mindset training?

A. We primarily depend on vendor tools and static rules; investigator training on emerging tactics is limited and not formally tracked.

B. We offer some training, usually vendor-led or post-incident, with limited documentation of outcomes.

C. We invest in internal expertise with planned training, documented materials, and some simulations, though feedback into procedures is ad hoc.

D. We deliberately design and document programs to develop adversarial mindset skills (simulations, case dissections, red-teaming), track participation and outcomes, and feed learnings back into procedures, rules, and models.

How consistently do you connect signals from data breaches, CIP/KYC at onboarding, identity and profile creation, account behavior, and bust-out events into a single view of synthetic identity risk?

A. Each stage (breach, onboarding, account behavior, collections) is handled independently; links are rare.

B. We sometimes connect stages when investigating large or escalated cases.

C. We have some capabilities to join data across stages for investigations and periodic analysis.

D. We intentionally design models, dashboards, and workflows around the full synthetic lifecycle—from breach-driven data exposure through onboarding, aging, and cash-out—both for detection and governance (including CIP/KYC obligations).

Scenario: Rapid Limit / Access Acceleration

A portfolio or product line (e.g., BNPL, cards, loans, B2B credit, or high-value subscription access) shows a cluster of accounts with perfect early behavior, rapid limit or access increases, and then coordinated high-value utilization or claims. How consistently is this treated as a potential synthetic or orchestrated fraud pattern, rather than simply “excellent customers”?

A. We would typically interpret this as strong performance and focus on growth rather than potential fraud.

B. We might notice the pattern, but follow-up depends heavily on individual analysts or one-off escalation.

C. We have rules or models that may flag this, though not always with an explicit synthetic/bust-out lens.

D. We have explicit patterns, playbooks, and review routines for this behavior as a potential synthetic / bust-out strategy across relevant products and channels.

How consistently does your team go beyond vendor materials (blogs, webinars, product updates) to study fraudster behavior, case files, and cross-industry attack patterns—and feed that insight back to analysts?

A. Most learning is vendor-led and product-focused; internal analysis is limited.

B. We occasionally review external cases or regulatory reports, typically after incidents or audits.

C. We regularly incorporate industry reports, law-enforcement cases, and peer learnings into our thinking.

D. We have a structured intelligence process that analyses adversary behavior, regulatory and case signals, and includes analyst feedback loops to update rules, models, training, and procedures.

How consistently do you intentionally test your own environment by simulating fraudster tactics (e.g., internal red-teaming, SecOps–FraudOps tabletop exercises, synthetic test scenarios)?

A. We rarely, if ever, test our environment from an adversarial perspective.

B. We run informal or ad hoc tests when time permits or when prompted by incidents.

C. We have some structured simulations or tabletop exercises annually, with basic action tracking.

D. We run regular, designed simulations and red-team exercises across fraud and cyber, with documented findings, remediation plans, retesting, and reporting into governance forums.

If you had to name one area where your fraud and cyber-fraud defenses feel “good, but not quite complete”, what would it be—and why?

(Feel free to include constraints such as data fragmentation, processes, regulatory burden, or resource limits.)

If you needed to reduce cyber-fraud and synthetic exposure by 30% in the next 12 months without buying new tools, under Board and regulatory scrutiny, what would you change first?