How Coffee Meets Bagel safeguards its community for users truly looking for love
Company Size
1,000+
Region
- America
Country
- United States
Product
- Sift
Tech Stack
- Machine Learning
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Brand Awareness
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Machine Learning
- Cybersecurity & Privacy - Application Security
Applicable Industries
- Software
Applicable Functions
- Business Operation
Use Cases
- Fraud Detection
Services
- Data Science Services
About The Customer
Coffee Meets Bagel is a leading dating application with a mission to help everyone find love. The platform takes a unique approach to the classic online dating experience by sending its users daily, high-quality matches curated by an ever-evolving algorithm. This approach eliminates the endless swiping that other dating apps rely on. With more than 150 million matches made to date, Coffee Meets Bagel is a platform where people go to find real relationships. However, the platform was facing challenges with fraudulent users creating fake profiles and engaging in romance scams, which was impacting the brand's integrity and the trust users had in the platform.
The Challenge
Coffee Meets Bagel (CMB) is a leading dating application that aims to provide a safe environment for its users to find real relationships. However, the integrity of its community was being compromised by fraudulent users creating fake profiles and engaging in romance scams. These fraudulent activities not only impacted the brand's integrity but also the trust users had in the platform. Fraudsters were sophisticated and quickly adapted to the rules-based systems and methodologies that CMB used to stop them. As the user base of CMB expanded, the company needed a solution that could adapt instantly, stay ahead of fraudsters, and scale as the business grew.
The Solution
Coffee Meets Bagel implemented Sift, a machine learning technology, to proactively detect scammers and fake profiles faster than with an internal system alone. By leveraging Sift’s global network and real-time risk assessments, Coffee Meets Bagel can now quickly, and in certain cases automatically, ban fraudulent users before they compromise the CMB dating community. Sift’s real-time machine learning model enables CMB to get accurate data quickly, thus allowing their review team to make faster, more informed decisions. Leveraging the Sift Score and Sift’s easy and intuitive tools, CMB can autoblock users and investigate suspicious behavior.
Operational Impact
Quantitative Benefit
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