Alaska Airlines selects Sift to mitigate fraud and manage chargeback disputes

Alaska Airlines is set to leverage Sift to automate digital risk decisions via its AI-enabled platform and Global Data Network, which processes over 1 trillion events per year, according to company data. Additionally, Sift’s solution focuses on allowing the airline to automatically include custom data, such as flight manifest information in chargeback representment, ensuring high dispute win rates and minimised manual workloads. This centres around adding both scale and efficiency throughout the entire fraud operations process. When commenting on the announcement, representatives from Alaska Airlines underlined their company’s commitment to offering a simplified and convenient experience for its guests from the time they book their flight until they land at their destination. By teaming up with Sift, Alaska Airlines plans to deliver increased trust across its digital channels, enabling guests to purchase their tickets and manage their travel itineraries without unnecessary friction or disruptions.

Furthermore, the decision to partner with Sift comes as fraud in the airline industry has substantially increased as the sector recovered from the COVID-19 pandemic. Sift’s officials mentioned that AI made fraud actors harder to detect when they attack businesses and consumers, with them becoming better equipped with the evolution of technology. By entering this collaboration, the airline can centre its efforts on fighting fraud through the use of AI and optimise the customer experience.

The alliance with Alaska Airlines comes shortly after Sift launched ThreatClusters, a data science tool for identifying fraud. To address current challenges in the financial landscape, the solution was developed to group companies with similar fraud trends into cohorts to accommodate variations in risk patterns, resulting in more precise fraud decisions. Also, ThreatClusters was set to support enterprises by merging industry-specific model insights and blending customer-specific risk models with a global model to produce industry-specific risk signals.