Identity Fraud Data
Buy and sell identity fraud data data. Fraud types, losses, and detection rates — the identity theft data for fraud prevention AI.
No listings currently in the marketplace for Identity Fraud Data.
Find Me This Data →Overview
What Is Identity Fraud Data?
Identity fraud data encompasses detailed information about fraud schemes, financial losses, detection methodologies, and prevention strategies used across financial services, e-commerce, and government sectors. This data type includes statistics on synthetic identity fraud, account takeover incidents, phishing vectors, and fraud detection rates that power machine learning models and risk assessment systems. The market covers fraud analytics platforms, authentication solutions, and governance frameworks designed to combat increasingly sophisticated identity theft techniques including deepfakes, credential exploitation, and coordinated account abuse. Organizations use this data to train fraud prevention AI, conduct underwriting assessments, and maintain regulatory compliance across GDPR, PSD2, AML/KYC, and PCI DSS frameworks.
Market Data
USD 52.06 billion
Global Fraud Detection Market Size (2026)
Source: Coherent Market Insights
USD 146.25 billion
Projected Market Size (2033)
Source: Coherent Market Insights
USD 48 billion
Global E-commerce Fraud Losses Annually
Source: BIIA
2.2%
Global Fraud Rate (2025)
Source: Sumsub
USD 20,001–USD 120,000 (20% of incidents)
Most Common Identity Fraud Loss Range
Source: BIIA
Who Uses This Data
What AI models do with it.do with it.
Banking and Financial Services
BFSI institutions use identity fraud data for secure customer onboarding, real-time fraud detection, regulatory compliance (AML/KYC), and transaction risk analysis. Banks leverage fraud statistics to train models detecting mid-to-high-value fraud events averaging USD 120,001–USD 300,000 per incident.
E-commerce and Digital Payments
Online merchants and payment processors analyze fraud data to reduce revenue losses (averaging 2.9% annually) and manage fraud management costs (10% of revenue). Platforms use detection rates and synthetic identity document fraud trends to protect against account takeover and payment fraud.
Government and Public Sector
Government agencies use identity fraud data to protect benefit programs, tax systems, and entitlements from synthetic ID exploitation and credential abuse. Detection frameworks help identify fraudsters using children's SSNs and synthetic identities in benefits claims.
AI and Machine Learning Development
Fraud prevention AI teams leverage historical fraud datasets, detection methodologies, and loss patterns to train advanced models. Data includes phishing statistics, deepfake-enabled schemes, and coordinated account abuse patterns needed for layered verification systems.
Pricing depends on the proposed terms
We do not have a verified, comparable price for your dataset. Consider the permitted uses, license term, exclusivity, provenance, coverage and quality. A seller asking price is a proposal, not an appraisal or guaranteed sale. Compare prices only when the source, date, currency, unit and license scope are known.
What Buyers Expect
What makes it valuable.valuable.
Accuracy in Loss Quantification
Buyers require precise fraud loss data broken down by incident size, sector, and fraud type. Data must distinguish between lower-value FinTech fraud (USD 0–USD 120,000 range) and higher-value banking fraud (USD 120,001+ range) to train differentiated risk models.
Detection Rate Transparency
Comprehensive datasets must include detection rates, false positive ratios, and time-to-detection metrics. Advanced fraud detection requires >90% adoption of data analytics, necessitating high-quality baseline detection statistics.
Regulatory Compliance Formatting
Data must align with GDPR data minimization, AML/KYC reporting structures, PSD2 Strong Customer Authentication requirements, and PCI DSS standards. Audit trails, consent documentation, and restricted access controls are non-negotiable.
Vector Diversity and Threat Currency
Buyers expect datasets covering multiple fraud vectors: phishing, synthetic identity, account takeover, deepfakes, and credential abuse. Data must reflect current threat landscapes and emerging fraud-as-a-service tactics.
Temporal Granularity
High-value datasets provide time-series fraud patterns, seasonal spending peak exploitations, and real-time monitoring capabilities for continuous risk assessment and incident tracking.
Potential applications and organizations
Who's buying.buying.
Advanced fraud analytics and identity verification for banking, insurance, and financial services. Operates in U.K. and U.S. mature markets using machine learning for real-time fraud detection systems.
Fraud detection and anti-money laundering solutions leveraging advanced analytics to combat complex fraud schemes in banking and financial services.
Cybersecurity and fraud detection frameworks for banking, insurance, and government services. Develops analytics-driven solutions for sophisticated fraud prevention.
Implement identity verification solutions to manage complex operations, secure large volumes of user identities, detect fraud in real-time, and maintain regulatory compliance. Expected to hold largest market share in 2025.
FAQ
Common questions.questions.
What types of fraud are covered in identity fraud datasets?
Identity fraud data covers multiple types: credit card fraud, bank fraud, phone/utility fraud, employment/tax fraud, synthetic identity fraud (blending real and fabricated data), account takeover, phishing attacks, and deepfake-enabled schemes. Data also includes coordinated fraud-as-a-service incidents and government benefit system exploitation.
Who are the primary buyers of identity fraud data?
Primary buyers include BFSI institutions (banks, insurers), e-commerce platforms, government agencies, payment processors, and AI/ML development teams. Large enterprises represent the largest segment, driven by need for complex operations management, regulatory compliance, and real-time fraud detection.
How much financial loss does identity fraud generate?
Global e-commerce fraud is projected at USD 48 billion annually. The most common loss range across all sectors is USD 20,001–USD 120,000 (20% of incidents). Banking-sector fraud skews higher, with 18% of incidents each in USD 120,001–USD 300,000 and USD 300,001–USD 470,000 brackets. Severe losses above USD 300,000 represent 27% of banking incidents.
What regulatory compliance frameworks impact fraud detection data?
Key frameworks include GDPR (data minimization, consent, audit trails), AML/KYC (customer verification, ongoing due diligence, SAR filing), PSD2 (Strong Customer Authentication and transaction risk analysis), and PCI DSS (cardholder data security). Fraud detection systems must integrate with these compliance requirements and maintain detailed logs for auditing.
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Describe your identity fraud data and the uses you are authorized to offer. Price, legal suitability, and buyer interest require separate evaluation. No match or sale is guaranteed.
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