Nearly half of all deepfake fraud losses worldwide, $1.73 billion, originated from social media platforms. Visualization

What This Visualization Shows

This data visualization displays "Nearly half of all deepfake fraud losses worldwide, $1.73 billion, originated from social media platforms." and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on **Methodology and sources**

This study used data from the AI Incident Database, [Resemble.AI](http://Resemble.AI), and the OECD to create a combined dataset covering deepfake incidents from January 2020 to June 2026. Incidents were included if they involved the generation of synthetic videos, images, or audio; were verified by media reports; and had clearly documented financial losses.

Each deepfake incident was classified by target country and origin. These figures represent a conservative estimate based on publicly reported data, and the source databases included records of deepfake incidents across multiple languages.

If multiple countries were mentioned in an incident with a specific financial loss, that loss was divided equally among the countries involved.

To avoid double counting, each incident was assigned to one primary origin category based on the main channel, tactic, or entry point described in the source material. The categories were defined as follows:

* Social media: cases originating on social media platforms; * Impersonation fraud: cases involving impersonation, synthetic facial verification videos, AI-generated face swaps, voice cloning, forged or altered IDs, fraudulent account openings, loan application fraud, or unauthorized access to wallets, bank accounts, or financial services; * Website: cases originating from fraudulent websites, including fake investment platforms, scam landing pages, or other web-based fraud schemes; * Fake job candidates: cases involving AI-generated resumes, face-swapping, deepfake video interviews, or other synthetic identity tactics used to infiltrate hiring processes; * Phone call: cases where deepfake-enabled voice cloning or impersonation was used during phone-based scams; * Video platform: cases originating on or using video platforms, including video conferencing tools or video-sharing platforms; * Messaging app: cases involving deepfake-enabled scams carried out through messaging applications; * Email: cases involving deepfake-enabled fraud delivered or initiated through email; * Other: cases that do not fit into the above categories, including novel schemes or unclassified methods., which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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Data Analysis and Insights

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Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.

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Significance and Applications

This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.

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The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.