A Major Airline Fraud Prevention Case Study | Nethone
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Overview of the problem

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 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.

Challenges raised by the customer

Reduce False Positives
Reduce False Positives

While high fraud rates can be detrimental to a company, false positives - a transaction labeled as suspicious despite being initiated by a legitimate customer - can lead to a huge negative impact on customer UX, resulting in lost custom.

Too many manual reviews
Too many manual reviews

The growing amount of time-consuming manual review traffic generated resulted in large financial costs and unnecessarily burdened the company’s fraud management team.

A full scope analysis for effective fraud prevention
A full scope analysis for effective fraud prevention

Rules-based systems can be ineffective in identifying and preventing all types of fraud as they focus primarily on knowing your customers (KYC). The customer wished to have an improved understanding of all the types of fraud affecting their business while having an effective means to prevent them.

Solution and benefits for the customer

Upon successful implementation of Nethone’s 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.

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, Nethone’s ML models and signals (triggers that indicate suspicious behavior indicative of a high probability of fraud) 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.

“When we started the cooperation with Nethone, we aimed to improve the automatic authentication of good customers, improving our customers’ experience and consequently lowering the cost of anti-fraud with manual analysis. In a short time, we were able to obtain encouraging results…”

Felipe Maia, Fraud Prevention Coordinator

The customer is now delighted to be fully effective in preventing fraud, understanding it is necessary to know your users (KYU) as opposed to knowing your customer (KYC). With KYU it is possible to ensure a frictionless experience for regular customers while blocking fraudsters before they can act.

The benefits of the customer’s improved anti-fraud setup have allowed the company to eliminate unnecessary revenue losses through a reduction in chargeback rates and false positives. Operational costs are down thanks to significantly reducing time-consuming manual reviews.


of fraudulent transactions detected during a fraud episode
of new total transactions rejected, significantly lower than before

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