---
title: Less false positives and more revenue growth for Azul airline
description: Learn how the Brazilian airline company Azul reduced their false positives rate and manual reviews while growing their revenue with fraud prevention.
image: https://blog.mangopay.com/hubfs/azul-case-study-thumbnail.png
---

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# Less false positives and more revenue growth for Azul airline

 Learn how the Brazilian airline company Azul reduced their false positives rate and manual reviews while growing their revenue with fraud prevention.

![azul-case-study-desktop](https://blog.mangopay.com/hubfs/azul-case-study-desktop.png) ![azul-case-study-thumbnail](https://blog.mangopay.com/hubfs/azul-case-study-thumbnail.png)

![Picture of Anda Kania](https://blog.mangopay.com/hs-fs/hubfs/download-anda-circle.png?width=24&name=download-anda-circle.png) [Anda Kania](https://blog.mangopay.com/en/home/author/anda-kania)

[Clients](https://blog.mangopay.com/en/home/tag/clients), [Nethone](https://blog.mangopay.com/en/home/tag/nethone) 

 Jul 29, 2024

Azul Linhas Aéreas is a Brazilian airline with the largest air network in Brazil, serving more than 100 national destinations, in addition to operating selected international routes to the United States and Portugal.

## The goal:  lower false positives rate and less manual reviews

The airline sought to enhance its existing anti-fraud capabilities, seeking an option that would work effectively and automatically, cutting operational costs by reducing time-consuming manual reviews. Additionally, they wanted an advanced solution that would identify and prevent all types of fraud affecting their business. There was a strong emphasis on providing a frictionless customer experience, from start to finish of the customer journey.

![azul-felipe-maia](https://blog.mangopay.com/hs-fs/hubfs/azul-felipe-maia.png?width=3840&height=1784&name=azul-felipe-maia.png)

Upon successful implementation of our advanced fraud detection and prevention solution with the help of our customer-centric approach, the customer quickly discovered their payments process was much safer than before. They were able to identify new types of fraud affecting their business - something that was not possible with their previous rules-based setup.

![azul-challenges](https://blog.mangopay.com/hs-fs/hubfs/azul-challenges.png?width=3840&height=1352&name=azul-challenges.png)

## The results: precise fraud detection powered by ML

The customer was impressed with the capabilities of behavioral biometrics and digital fingerprinting, backed up by machine learning (ML) models to distinguish between good and bad customers and fraud actors. During a specific fraud attack episode, our ML models and signals detected 89% of fraudulent transactions, compared to 16% detected by the rules-based system, through discrepancies between user account purchase history and use of multiple email accounts. Such detailed analysis is only possible with an advanced fraud solution.

![azul-case-studies-achievements](https://blog.mangopay.com/hs-fs/hubfs/azul-case-studies-achievements.png?width=3840&height=1096&name=azul-case-studies-achievements.png)

The customer’s improved anti-fraud setup has allowed the company to prevent** revenue losses** through a reduction in chargeback rates and false positives. **Operational costs are down** thanks to reduced time-consuming manual reviews.

![cta-fraud-prevention-mangopay](https://hubspot-no-cache-eu1-prod.s3.amazonaws.com/cta/default/26095004/interactive-97810721527.png)

Ready to detect fraud just like Azul? [Get in touch](https://mangopay.com/contact) to learn more.

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