Big Data Fraud Detection

Trusted By Companies Like

“Yelp is committed to protecting the integrity of our content. DataVisor’s security analytics are an important part of our spam detection system, and help prevent malicious accounts from interacting with real users and businesses.”

– Jim Blomo, Engineering Manager, Yelp

“DataVisor was able to find numerous malicious campaigns so that we could eliminate fraudulent accounts before they could do any damage. This was vital to sustain the rapid user growth of our platform.”

– Yan Tang, CEO Momo

“DataVisor has been valuable in our work to preemptively detect malicious behavior and ensure a safe experience for the people using Pinterest.”

– Sriguru Chakravarthi, Head of Trust & Safety Engineering, Pinterest

“At IGG, we are focused on growing our global user base of gamers. DataVisor helps us ensure new installs are from legitimate users, thereby maximizing our ROI from our user acquisition marketing and maintaining the quality of our gaming community.”

– Mark Zhang, CTO & SVP, IGG

Big Data Fraud Detection At Massive Scale

Big Data Fraud Detection for 500B events

639,000,000,000+

Events analyzed to date

2,000,000,000+

Users protected globally

140,000,000+

Bad accounts detected to date

Big Data Fraud Detection

The rapid expansion of consumer-facing online services has led to an explosion of user accounts, ushering in the “billion user era”. Well-organized attack campaigns are using this growth to their advantage, creating armies of fake and compromised accounts to hide in the shadows and conduct fraud against banks, web sites and mobile apps. It is challenging for trust and safety teams to stay ahead of these fraudsters since rules and models need constant maintenance and are always reacting after the damage has been done. Our approach to big data fraud detection gives you a leg up.

What We Do

DataVisor is the leading big data fraud detection solution utilizing unsupervised analytics to identify malicious account campaigns before they conduct any damage to banks, consumer-facing web sites and mobile apps. Our unique approach to big data fraud detection does not rely on prior knowledge of attack patterns – “training data” in data science speak.