Sift Case Studies How PayMongo minimized fraud losses and scaled securely by 10-20x

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	How PayMongo minimized fraud losses and scaled securely by 10-20x Sift

How PayMongo minimized fraud losses and scaled securely by 10-20x

Sift

Technology Category	Analytics & Modeling - Machine Learning
Applicable Industries	Finance & Insurance
Applicable Functions	Business Operation
Use Cases	Fraud Detection
Services	Data Science Services
Challenge	During the early stages of the company, PayMongo encountered fraud attacks that resulted in financial losses, including an alarming 4% dispute rate. It was crucial for the startup company to prevent this fraudulent activity in order to enable their merchants’ success and scale their own business. In their search for the perfect fraud solution, PayMongo was introduced to Sift at a Y Combinator event and agreed to an assessment. Following the review, PayMongo concluded Sift Payment Protection was an ideal fit for what they were looking for in a fraud tool. Read More
About Customer	PayMongo is a financial technology company that provides simple, modern payments for forward-thinking businesses. The company helps businesses in the Philippines accept online payments from multiple channels quickly and easily, in a matter of minutes. Their mission is to become the invisible engine of commerce that gives everyone the opportunity to participate and succeed in the rapidly transforming digital economy. Read More
Solution	Members of PayMongo’s Fraud Operations team use Sift for transaction monitoring, case management, setting workflow rules, and routing payments. The team utilizes almost every feature of the Payment Protection product, including automated workflows to set up review queues, as well as create rules and thresholds. The team also relies heavily on insights within the console to monitor workflow metrics, reports, and team performance. Sift’s machine learning capabilities help PayMongo automatically block high-risk and possibly fraudulent transactions. The team tailor-fits workflows according to the observed fraudulent patterns on their platform and is able to update them as fraud trends evolve. For additional support, PayMongo consults with senior engineers, fraud experts, and others at Sift for guidance and consultation. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Applicable Industries

Finance & Insurance

Applicable Functions

Business Operation

Use Cases

Fraud Detection

Services

Data Science Services

Challenge

During the early stages of the company, PayMongo encountered fraud attacks that resulted in financial losses, including an alarming 4% dispute rate. It was crucial for the startup company to prevent this fraudulent activity in order to enable their merchants’ success and scale their own business. In their search for the perfect fraud solution, PayMongo was introduced to Sift at a Y Combinator event and agreed to an assessment. Following the review, PayMongo concluded Sift Payment Protection was an ideal fit for what they were looking for in a fraud tool.

About Customer

PayMongo is a financial technology company that provides simple, modern payments for forward-thinking businesses. The company helps businesses in the Philippines accept online payments from multiple channels quickly and easily, in a matter of minutes. Their mission is to become the invisible engine of commerce that gives everyone the opportunity to participate and succeed in the rapidly transforming digital economy.

Solution

Members of PayMongo’s Fraud Operations team use Sift for transaction monitoring, case management, setting workflow rules, and routing payments. The team utilizes almost every feature of the Payment Protection product, including automated workflows to set up review queues, as well as create rules and thresholds. The team also relies heavily on insights within the console to monitor workflow metrics, reports, and team performance. Sift’s machine learning capabilities help PayMongo automatically block high-risk and possibly fraudulent transactions. The team tailor-fits workflows according to the observed fraudulent patterns on their platform and is able to update them as fraud trends evolve. For additional support, PayMongo consults with senior engineers, fraud experts, and others at Sift for guidance and consultation.

Impact #1	Automatically blocking high-risk, suspicious, and fraudulent transactions
Impact #2	Saving time and money by reducing manual labor
Impact #3	Improving operational efficiency and securing more revenue

Impact #1

Automatically blocking high-risk, suspicious, and fraudulent transactions

Impact #2

Saving time and money by reducing manual labor

Impact #3

Improving operational efficiency and securing more revenue

Benefit #1	Reduction in manual review, resulting in saved time and money
Benefit #2	Fraud losses and fraudulent chargebacks below threshold
Benefit #3	Handling 10-20x more transactions safely and securely

Benefit #1

Reduction in manual review, resulting in saved time and money

Benefit #2

Fraud losses and fraudulent chargebacks below threshold

Benefit #3

Handling 10-20x more transactions safely and securely

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Overview

How PayMongo minimized fraud losses and scaled securely by 10-20x

Operational Impact

Quantitative Benefit