Payoneer Case Study
Customer Company Size
Large Corporate
Country
- Worldwide
- United States
Product
- Iguazio data science platform
- Payoneer digital payment platform
Tech Stack
- Machine Learning
- Predictive Analytics
- Real-Time Data Collection
- Python
- Spark
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Cost Savings
- Digital Expertise
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Finance & Insurance
- Retail
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Fraud Detection
- Predictive Quality Analytics
- Real-Time Location System (RTLS)
Services
- Data Science Services
- System Integration
About The Customer
Payoneer’s digital platform streamlines global commerce for millions of small businesses, marketplaces and enterprises from 200 countries and territories. Their innovative end-to-end platform enables businesses to expand their products and services and reach clients worldwide. As such, millions of individuals and businesses around the world rely on Payoneer to make and receive cross-border payments. In the face of an increasingly dangerous cybersecurity landscape and the pervasiveness of fraud and money laundering activities, Payoneer came up with an innovative initiative to keep their customer’s money safe.
The Challenge
Payoneer, a digital payment platform, needed a way to serve its AI/ML fraud predictive/preventative models against fresh, real-time data to provide their customers with a safer payment experience. The company was using a retroactive approach that detected fraud attempts after the fact, which meant customers could only block users after a (possibly successful) fraud attempt. This approach had several limitations including the inability to track fraud attempts across complex networks, lack of advanced analytics and log correlation to identify anomalies, and a negative impact on customer experience and satisfaction. Payoneer needed a solution that leveraged sophisticated algorithms to track multiple parameters and detect fraud within complex networks. While Payoneer had built sophisticated machine learning models, these only worked offline and could not be used against real-time threats.
The Solution
Payoneer chose Iguazio to bring its most intelligent data science strategies to life. The data science platform allowed Payoneer to deploy and run its predictive machine learning models in real-time. This resulted in a scalable and reliable fraud prediction and prevention tool that could analyze fresh data in real-time and adapt to new threats, thus making fraud attacks almost impossible on Payoneer. Payoneer deployed Iguazio to power its predictive ML models and serve them in real-time against fresh data. This facilitated continuous monitoring of suspicious patterns and prevention of fraud with minimum false positives. The production pipeline is based on Python code that is running via Nuclio functions and Spark framework. This approach doesn’t require conversion of code from Python to Java or others. Python is used all the way up to live deployment.
Operational Impact
Quantitative Benefit
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