Sift
- Security
- 1 Product
- 1 Case Study
Protect your business from fraud and fuel your growth through the power of digital trust.
We believe trust and safety are fundamental to every online interaction. As the pioneers of Digital Trust & Safety, we help more than 12,000 sites and apps navigate the fine balance between growing revenue and protecting their business.
We believe users are innocent until proven guilty. A business’s good customers should never be turned away or made to jump through hoops. We make it easy for companies to remove friction and delight their customers, while keeping bad actors out.
We believe legacy technologies are holding businesses back. We were the first to use machine learning for fraud prevention, and our customers see the results every day. The speed, accuracy, and scalability of our solution is unrivaled.
The time to change is now. Customer expectations are higher than ever. Competition is fierce. Fraudsters innovate and scale as fast as businesses. That’s why thousands of websites and apps, from digital disrupters to Fortune 500 companies, partner with Sift.
- https://sift.com/
- Headquarters San Francisco Bay Area
- Year Founded 2011
- Current Funding $106.6M
- Company Size 51-200 employees
- Specialties Technology & Internet, Security, Machine learning, Big data, Ecommerce, Security, Fraud detection
In 2011, Sift Science disrupted the fraud prevention industry with a first-of-its-kind machine learning approach that accurately predicted fraud and defended against online abuse in real time.
Today, our Live Machine Learning™, global trust network, and automation technologies fuel the business growth of thousands of websites and apps, allowing them to expand into new markets; while protecting their customers from all vectors of fraud and abuse.
Products and Case Studies
Sift Science
Every day, businesses worldwide rely on Sift Science to automate fraud prevention, slash costs, and grow revenue.
Case Study : Seatgeek
Sift Science enabled SeatGeek to accurately predict fraud trends while identifying legitimate users.
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