How Cozy creates a safe platform for renters and landlords
公司规模
SME
地区
- America
国家
- United States
产品
- Cozy Platform
技术栈
- Sift
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Customer Satisfaction
技术
- 平台即服务 (PaaS) - 连接平台
适用功能
- 商业运营
用例
- 欺诈识别
服务
- 系统集成
关于客户
Cozy is a Portland, Oregon-based startup that offers a platform to streamline interactions between property managers and renters. Using this consolidated system, renters can connect with landlords, apply for properties, and pay their rents through the simple web interface, accessible on desktop or mobile. On the landlord side, Cozy makes recurring payments and property management simple and automatic. Additionally, the platform allows property managers to market their rentals and screen their prospective tenants online. With a commitment of making renting easy for everyone involved, Cozy processes close to $1 billion in rental payments every year. The company boasts 350,000+ property managers, landlords, and tenants in cities throughout the United States.
挑战
Cozy, a startup that offers a platform to streamline interactions between property managers and renters, was facing a significant challenge with fraud. With listings and renters in 13,000 cities nationwide, the company was experiencing both payment fraud and content abuse in the form of fake rental listings. Fraudsters were using fake rentals to ask for wire deposits from unsuspecting renters. As the company expanded its payment options beyond ACH to credit card, the problem only grew. Cozy needed a solution that could keep up with their wide user base and prevent fraud before it resulted in losses.
解决方案
Cozy brought on Kevin Collins and leveraged his experience in tracking fraud with online communities. Kevin pushed for a machine learning solution that could scale with the business and be predictive in its modeling. After trying a few different solutions that didn’t work for Cozy, Kevin found Sift. He was able to demonstrate Sift’s immediate value to the company, and a quick integration followed. Now, although business has grown, fraud rates have remained low. Kevin is efficient in his fraud management, automating based on Sift Score. When further investigation is required, he can do so effortlessly with the Sift Console, where he explores connected accounts and account activity.
运营影响
数量效益
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