Data Science helps Reducing Fraud and Saves $25 Million per Year
A leading North American video rental company saves $25 million per year by implementing two fraud identification algorithms.
A leading North American video rental company managed 40 thousand automated kiosks throughout the United States and Canada processing over 2 million rentals per day. The company lost over $100 million annually due to fraudulent activity.
Identify good vs fraudulent events. With volume of over 2 million rentals per day, identifying good vs fraudulent activity was not possible using their existing systems.
We designed and developed two regression algorithms to better identify fraudulent activity in real-time. The first algorithm focused on the established customer, and the second algorithm focused on the new customers. The output of these two algorithms quantified the likelihood of fraudulent transactions. Transaction data was integrated with multiple other data sources to create accurate quantifications.
Data Collection & Automation
Retail Transactional Data
Demographics data via 3rd party
Retail Store Metadata
Customer achieved annual savings of $25 million by utilizing data science techniques to help identify and prevent fraudulent transactions.
At a glance
Need to reduce losses by minimizing fraudulent transactions.
Automated data collection
Implementation of two regression algorithms that determine probability of fraudulent transactions resulted in annual savings of $25 million.
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PROOFS OF CONCEPT PER YEAR
BUSINESS FROM REPEAT WORK
Technologies We Support
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