Download PDF
Payoneer Case Study
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
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.
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 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
Related Case Studies.
Case Study
Improving Production Line Efficiency with Ethernet Micro RTU Controller
Moxa was asked to provide a connectivity solution for one of the world's leading cosmetics companies. This multinational corporation, with retail presence in 130 countries, 23 global braches, and over 66,000 employees, sought to improve the efficiency of their production process by migrating from manual monitoring to an automatic productivity monitoring system. The production line was being monitored by ABB Real-TPI, a factory information system that offers data collection and analysis to improve plant efficiency. Due to software limitations, the customer needed an OPC server and a corresponding I/O solution to collect data from additional sensor devices for the Real-TPI system. The goal is to enable the factory information system to more thoroughly collect data from every corner of the production line. This will improve its ability to measure Overall Equipment Effectiveness (OEE) and translate into increased production efficiencies. System Requirements • Instant status updates while still consuming minimal bandwidth to relieve strain on limited factory networks • Interoperable with ABB Real-TPI • Small form factor appropriate for deployment where space is scarce • Remote software management and configuration to simplify operations
Case Study
How Sirqul’s IoT Platform is Crafting Carrefour’s New In-Store Experiences
Carrefour Taiwan’s goal is to be completely digital by end of 2018. Out-dated manual methods for analysis and assumptions limited Carrefour’s ability to change the customer experience and were void of real-time decision-making capabilities. Rather than relying solely on sales data, assumptions, and disparate systems, Carrefour Taiwan’s CEO led an initiative to find a connected IoT solution that could give the team the ability to make real-time changes and more informed decisions. Prior to implementing, Carrefour struggled to address their conversion rates and did not have the proper insights into the customer decision-making process nor how to make an immediate impact without losing customer confidence.
Case Study
Digital Retail Security Solutions
Sennco wanted to help its retail customers increase sales and profits by developing an innovative alarm system as opposed to conventional connected alarms that are permanently tethered to display products. These traditional security systems were cumbersome and intrusive to the customer shopping experience. Additionally, they provided no useful data or analytics.
Case Study
Real-time In-vehicle Monitoring
The telematic solution provides this vital premium-adjusting information. The solution also helps detect and deter vehicle or trailer theft – as soon as a theft occurs, monitoring personnel can alert the appropriate authorities, providing an exact location.“With more and more insurance companies and major fleet operators interested in monitoring driver behaviour on the grounds of road safety, efficient logistics and costs, the market for this type of device and associated e-business services is growing rapidly within Italy and the rest of Europe,” says Franco.“The insurance companies are especially interested in the pay-per-use and pay-as-you-drive applications while other organisations employ the technology for road user charging.”“One million vehicles in Italy currently carry such devices and forecasts indicate that the European market will increase tenfold by 2014.However, for our technology to work effectively, we needed a highly reliable wireless data network to carry the information between the vehicles and monitoring stations.”
Case Study
Ensures Cold Milk in Your Supermarket
As of 2014, AK-Centralen has over 1,500 Danish supermarkets equipped, and utilizes 16 operators, and is open 24 hours a day, 365 days a year. AK-Centralen needed the ability to monitor the cooling alarms from around the country, 24 hours a day, 365 days a year. Each and every time the door to a milk cooler or a freezer does not close properly, an alarm goes off on a computer screen in a control building in southwestern Odense. This type of alarm will go off approximately 140,000 times per year, equating to roughly 400 alarms in a 24-hour period. Should an alarm go off, then there is only a limited amount of time to act before dairy products or frozen pizza must be disposed of, and this type of waste can quickly start to cost a supermarket a great deal of money.
