How Traveloka increased real-time bookings and stopped ATO attempts

• 分析与建模 - 大数据分析
• 分析与建模 - 机器学习

• 商业运营
• 销售与市场营销

• 欺诈识别

• 数据科学服务

Traveloka, a leading platform for booking flights and hotels in Southeast Asia, was facing two main types of abuse: payment fraud from stolen credit cards and account takeover (ATO) from stolen credentials and social engineering schemes. Both these problems led to financial loss and, more importantly, damaged user trust and brand reputation. Traveloka had an internal team dedicated to fraud and risk, developing a series of elaborate fraud rules that attempted to provide an automated first screening of all orders. However, as the range of customers on the site changed, Traveloka’s rules-based system couldn’t keep up. They experienced many false positives that were blocking good customers and their orders, leading to poor customer experience. On the ATO side, static rules were missing a lot of cases, weren’t able to adapt quickly enough to emerging trends, and resulted in a lot of false positives, blocking legitimate users from accessing the site.

Traveloka is a Jakarta-based company that operates Indonesia’s number one platform for booking flights and getting great deals on hotels. With an ever-growing number of visitors to the site, this company has grown to offices in Thailand, Malaysia, Singapore, Vietnam, and the Philippines. Traveloka’s business is booming in the Southeast Asian market and – following on the heels of legitimate customers – fraudsters are creeping into the fold. As a Sift customer, Traveloka’s volume of fraud is miniscule, and the Traveloka team is committed to keeping that fraud rate low.

Traveloka began investigating machine-learning based solutions to replace their rules-based system. Big data was already an integral part of Traveloka’s customer service, marketing, and fraud operations. And now the product team – headed by Wayan Perdana – was tasked with finding an adaptive solution that reduced false positives, identified more ATO incidents, and could increase conversions. He turned to Sift because of its sophisticated machine learning platform that scales with growth, adapts to new fraud patterns, and accurately separates good users from bad. Traveloka integrated with Sift to detect both types of fraud. Traveloka has two separate, custom machine learning models that leverage behavioral data – one for payment abuse and the second for ATO – to identify suspicious cases.

• Traveloka was able to accept twice the amount of orders that were previously blocked by their rules system.
• They crafted a better customer experience that reduced traffic to 3D Secure by 3x.
• They have seen fewer ATO cases overall thanks to Sift’s detection abilities.

• 3x Less traffic to 3D Secure
• 2x More orders accepted