With over 10 million logins everyday, facilitating fund transfer in 26 different currencies, processing more than 1.1 petabytes of data, PayPal is one of the top payment processing transaction companies on the web. In terms of market capitalization, PayPal is among the top 5 payment companies. The reason why it has grown to such an extent may be attributed to its extensive use of Big Data analytics and data science. We’ll visit on the aspects of data science and Big Data analytics techniques used by PayPal which is key in enriching customer experience.
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Through cloud computing, payments of auction websites and vendors are done in a safe and secure manner by PayPal. The merchants are in fact the real customers of the PayPal and the customers of the merchants are the indirect customers of PayPal. Due to PayPal’s enhanced predictive capabilities merchants’ customer experience is phenomenally improved.
How PayPal extensively benefits from Hadoop?
Regardless of the data format, PayPal processes every chunk of its data using HBase and Hadoop. PayPal is binding conventional databases and Hadoop to improve itself as a specialist service provider to its clients. Hadoop works with conventional data platforms at PayPal to meet different business requirements like client sentiment analysis, market segmentation and to detect fraud. The conjunction of Hadoop with conventional data platforms, enables data scientists to run queries for testing of hypothesis and research on the information stored in HDFS.
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Considering the need for advanced security mechanisms for Hadoop, PayPal anonymizes all information before it is stored in Hadoop. PayPal utilizes semi-structured data in Hadoop, for Big Data analytics and business intelligence projects and stores it in the cloud with the goal that PayPal representatives over the globe can get to it. It gathers more than 20 terabytes of log information daily. This is extremely useful in recommendation engine, sending out real-time location based offers, sentiment analysis, event analytics and customer segmentation.
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Fraud detection in PayPal
PayPal is fabricating new fraud analytics system and changing the current one by consolidating different open source technologies like Hadoop and Spark along with online caching, applying machine learning algorithms, and making use of detective personnel. Once the machine learning models recognize the likelihood of a fraud the detective makes it sure to the PayPal whether it is a fraud or not by thorough investigation.
Risk Management is superfast in that the machine learning algorithms settle on a choice in milliseconds on whether it is a decent transaction or a fraud one attempting to take part in deceitful activity. If a client is recognized as trustworthy and dependable his transactions are executed fast. The system slows down to garner more data along with performing detailed analysis if the algorithm suspects the transaction to be illicit.
Latest activity, buying history, cookie data, and hundreds of other variables are assessed by the machine learning algorithms to detect fraud transactions. The results of these analyses are compared to external data provided by authentication providers for any correlation. Take for instance; a single account would be flagged if it shows as being operated from different locations worldwide in very less time.
How highly relevant ads are possible in PayPal?
Based on the shopping medium like online or in-app, the analytic algorithms use past purchase history which is helpful in saving client’s money and also to provide high transaction volumes for merchants. To send relevant offers, discounts and personalized ads; PayPal ties purchase history, user activity across various sites, customer tastes using Big Data analytics to achieve this. With a whopping 69% accuracy PayPal is able to estimate as to where the customers are likely to spend their money through their predictive models.
The algorithms of NLP (Natural Language Processing) are behind the success of PayPal’s extraordinary customer service. PayPal uses Hadoop based text mining in topic modeling, predictive modeling, clustering, influence scoring. Without text mining, PayPal can’t recognize from a product purchase whether a customer likes the product or likes the brand of the product. With text mining however this can be assessed and relevant recommendations can be conveyed to the client.
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Conclusion
PayPal has become an expert in using customer’s search and transactional data to improve their predictive abilities with record success. PayPal aims to be used weekly, daily rather than just a payment option in a retailer’s site. Venmo is mobile money transfer app and Xoom is used for international remittances both of which are acquired by PayPal. PayPal hopes to provide bill payments, ATM services and integrate the services from its aforesaid acquisitions. Judging by its growth trajectory PayPal is set to taste stupendous success in the future. The core reason for such a feat would be that it efficiently uses Big Data analytics.
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