Fraud Losses, AI-Driven Scams, and Rising Cyber Risk Reshape Global Digital Payments and E-Commerce Security
This report analyzes the evolving landscape of cybersecurity, fraud, and risk in digital payments and online commerce. The report examines the global scale of fraud losses, shifting attack vectors such as social engineering and identity fraud, the growing influence of artificial intelligence in both fraud execution and detection, and the expanding economic and regulatory implications of digital financial crime across payment and commerce ecosystems. It provides structured, source-based insights into fraud exposure, cybersecurity investment, AI-enabled threat dynamics, and the strategic responses shaping the global digital commerce security landscape.
Key Highlights
- Global E-Commerce fraud losses are forecast to more than double from over USD 40 billion in 2024 to more than USD 100 billion by 2029, reflecting the growing financial exposure of digital commerce ecosystems as online transactions expand globally.
- Financial institution fraud losses are projected to rise by over 150% from less than USD 25 billion in 2025 to more than USD 55.3 billion by 2030, indicating increasing operational and financial pressure on banks and payment providers as fraud activity becomes more complex and scalable.
- Average individual crypto scam payment values increased by more than 250% from less than USD 800 in 2024 to over USD 2,750 in 2025, highlighting escalating transaction-level fraud severity and the growing financial impact of crypto-related scams.
Digital Commerce Expansion Increases Exposure to Fraud Losses
Fraud losses are rising alongside the expansion of digital commerce and payment transactions. Global E-Commerce fraud losses are expected to more than double by 2029, while fraud losses affecting financial institutions are forecast to increase sharply by 2030 as digital payments scale. Over the next decade, cumulative global card payment fraud losses are estimated to reach over USD 400 billion, reflecting the growing exposure linked to online and card-not-present transactions.
Manipulation-Driven Scams and Identity Abuse Reshape Fraud Activity
Fraud is increasingly shifting from technical compromise toward manipulation-driven schemes in which victims authorize transactions themselves. Social engineering, impersonation tactics, and identity misuse are becoming central drivers of fraud losses across digital commerce and payment ecosystems. Many fraud attempts now originate through social media, messaging platforms, or phone calls before leading to financial transactions.
Artificial Intelligence Expands Both Fraud Risks and Defensive Capabilities
Artificial intelligence is reshaping the fraud landscape by enabling more advanced attacks while also strengthening detection tools. Generative AI can support scalable phishing campaigns, deepfake impersonation, and automated scam operations. At the same time, financial institutions and digital platforms are adopting AI-driven detection systems based on behavioral analytics, machine learning models, and real-time risk scoring.
Key Questions Answered
- How are fraud, scams, and cyber risks reshaping digital payments and E-Commerce ecosystems globally in 2026?
- Which fraud typologies are becoming most prevalent across digital payments and online commerce globally in 2025?
- What role is artificial intelligence playing in the global fraud landscape in 2026?
- How are crypto scams influencing the global fraud landscape in 2025?
- What structural factors are increasing fraud exposure in global digital commerce ecosystems?
Table of Contents
1. Key Takeaways2. Management Summary
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- Alibaba
- Amazon
- JD.com
- Temu
- Shopee
- SHEIN
- TikTok Shop
- Visa
- Mastercard
- American Express
- PayPal
- Klarna
- Adyen
- Stripe
- Worldline
- Revolut
- Wise
- Apple
- Microsoft
- OpenAI
- SAP
- IBM
- NVIDIA
- MercadoLibre
- EBANX
- Flutterwave
- M-Pesa
- Luckin Coffee
- Alipay
- Alipay+
- LianLian
- Taobao
- Tmall
- DeepSeek
- Zhipu AI
- LVMH

