Ai Events | Airline Information’s Post

Counting on AI for a decline in false declines #ATPS2025 AI's ability to analyse vast amounts of data, learn from patterns, and make real-time decisions is crucial in reducing false declines in #payment authorisation requests. Despite the significant advancements in AI and payment technologies, reducing false declines remains a complex challenge. Here are some of the current obstacles: ·         Quality of data: The quality of data used to train AI models is crucial. Incomplete or inaccurate data can lead to biased models and inaccurate predictions. ·         Agile fraudsters: #Fraudsters are constantly adapting their techniques to evade detection. AI models must be continuously updated to keep pace with these evolving threats. ·         UX issue: While reducing false declines is important, it's also essential to avoid excessive security measures that can create friction for legitimate customers. Also, Overly sensitive fraud detection models can lead to false positives. ·         Complexity in cross-border transactions: Cross-border transactions involve multiple parties and currencies, making it more difficult to identify fraudulent activity. ·         Dealing with legacy tech: Integrating AI-powered fraud detection solutions with existing legacy systems can be complex and time-consuming. What are airlines working on to count on AI and at the same time dealing with such challenges? By Ritesh Gupta, Ai Events Christopher Staab Sift Forter SEON Cybersource CyberArk Accertify, Inc. Nethone (acquired by Mangopay) Visa Mastercard Adyen Chargebacks911 Darwinium Kasada DataDome Arkose Labs Ravelin Technology #fraudprevention #machinelearning #travel #airlines

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