# How does Sift use machine learning to adapt to evolving fraud patterns?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">How does Sift use machine learning to adapt to evolving fraud patterns?</p>

##### Post Metadata
- Posted at: 4 months ago
- Author title: Digital Content Manager
- Net upvotes: 2


## Comments
### Comment 1

Sift’s machine learning approach is built around real-time risk evaluation, network intelligence, and continuous model refinement. Sift uses custom, industry, and global models fed by the Global Data Network, which processes more than 1T annual events across 34k+ sites and apps and 16k+ risk signals. That breadth helps Sift identify emerging fraud patterns faster and add context beyond any single business’s own data.

The Sift platform combines AI/ML models, real-time risk scoring, identity graph insights, and decisioning tools in one system rather than relying on static rules alone. Interconnected risks across industries and geographies continuously fuel and refine the Global Data Network and Sift’s AI/ML models.

Sift&#39;s Clearbox Decisioning gives fraud teams visibility into why a score or decision was made, including explainable signals and model context, instead of forcing them to rely on a black-box output.

##### Comment Metadata
- Posted at: 3 months ago




## Related Product
[Sift](https://www.g2.com/products/sift-s-ai-powered-fraud-decisioning-platform-sift/reviews)

## Related Category
[Fraud Detection](https://www.g2.com/categories/fraud-detection)

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