Fraud prevention and identity verification require different tools for different jobs. facesearching and Ekata represent two distinct approaches to verifying identity: facesearching is a face search engine that lets you find someone by photo across 100+ public platforms, while Ekata is a digital identity verification and fraud prevention platform that uses data signals — phone numbers, email addresses, IP addresses, and physical addresses — to assess identity risk. This comparison examines the key differences between reverse face search with facesearching and data-driven identity verification with Ekata, helping you determine which tool is right for your fraud prevention needs. For more comparison guides, see our analysis of facesearching vs IDnow.
Overview: facesearching vs Ekata
facesearching is a reverse face search tool for consumers and small businesses. Upload a photo, and the tool scans over 100 platforms — social media, news sites, video platforms, and professional networks — to reveal where that face appears on the public web. facesearching is designed for individuals who need to verify someone's online identity by understanding their digital footprint. Ekata, acquired by Mastercard in 2021, is an enterprise identity verification platform that uses data science and machine learning to assess identity risk. Ekata's core products include the Identity Check API (which validates name, phone, email, address, and IP combinations), the Identity Network (which analyzes behavioral and transactional patterns), and Pro Insight (a manual review tool for fraud analysts). Ekata serves e-commerce merchants, financial institutions, payment processors, and marketplaces.
Technology Comparison
facesearching's technology is image-based: it uses face search algorithms to match facial features against publicly available images across the internet. The technology is visual — it works with photos, not data points. This makes it uniquely suited for cases where you have a photo but no other identifying information. Ekata's technology is data-based: it analyzes identity data signals — phone number validity, email address reputation, address verification, IP geolocation, and behavioral patterns — to generate a risk score. Ekata's machine learning models are trained on billions of transactions and identity data points. The key difference is that facesearching starts with a face and finds digital identity, while Ekata starts with identity data and assesses risk.
Data Sources and Coverage
- facesearching data sources: Scans 100+ public platforms including social media, news sites, blogs, and video platforms. Data is drawn from publicly available web content. Coverage is global, with strength in regions with high social media penetration.
- Ekata data sources: Uses proprietary identity data from billions of transactions, government and authoritative data sources, and the Mastercard network. Data includes phone, email, address, and IP intelligence. Ekata does not search social media or public web content.
- Key difference: facesearching reveals a person's visual digital identity — their photos, profiles, and online presence. Ekata reveals a person's data identity — their contact information, transaction history, and risk signals.
Pricing Comparison
facesearching offers transparent, consumer-friendly pricing with free preview searches and affordable paid options for full results. Individuals and small businesses can start using facesearching immediately without contacting a sales team, signing a contract, or committing to minimum volumes. Ekata operates on an enterprise pricing model with per-API-call fees, volume commitments, and annual contracts. Pricing is not publicly listed and requires engaging with the Mastercard sales team. Ekata is designed for businesses processing high volumes of identity verifications — e-commerce platforms, payment processors, and financial institutions. For individual users, small businesses, and anyone who needs to perform occasional identity checks, facesearching is dramatically more accessible and affordable.
Best Use Cases: Side-by-Side Comparison
- Transaction fraud prevention: Ekata wins. For e-commerce merchants who need to assess the risk of every transaction in real-time, Ekata's data-driven approach is the industry standard.
- Online dating identity verification: facesearching wins. Verify a match's photos before meeting. Ekata's data-based approach is not relevant for this use case.
- Payment processing risk assessment: Ekata wins. Payment processors need to analyze identity data signals at scale, which is Ekata's core competency.
- Social media investigation: facesearching wins. Discover a person's online presence across 100+ platforms. Ekata does not search social media.
- Manual fraud review: Both tools win in different ways. Ekata Pro Insight provides fraud analysts with data signals. facesearching provides visual identity verification through face search.
- Small business partner verification: facesearching wins. Verify business partners, contractors, and employees through face search without enterprise contracts.
The Complementary Approach: Using Both Tools
For organizations with comprehensive fraud prevention needs, facesearching and Ekata can be used as complementary tools in a layered defense strategy. Ekata provides the automated, data-driven risk assessment layer: it analyzes identity data signals in real-time to flag high-risk transactions for manual review. facesearching provides the visual investigation layer for those flagged transactions: a fraud analyst can use facesearching to investigate the person behind the transaction, verify their online identity, and discover red flags such as fake social media profiles or stolen photos. This two-layer approach creates a more robust fraud prevention system than either tool alone. To try facesearching, visit the facesearching home page. For another comparison, see facesearching vs Persona Identities.
Ekata tells you if a transaction is risky. facesearching tells you if a person is real. Together, they give fraud teams the complete picture.