在此影片中,Mastercard 網路與情報技術長 (CTO) Manu Thapar 介紹了 Mastercard 如何使用 Amazon Web Services (AWS) 人工智慧 (AI) 和機器學習 (ML) 服務來提高全球詐欺偵測能力。該解決方案助力 Mastercard 偵測到之前三倍的詐欺交易,並將誤報率降低十倍,從而為商戶節省了數十億美元的開支,並為全球客戶提供更理想的體驗。

「這種詐欺偵測能力的提升和誤報率的減少意味著商家擁有更實用的解決方案,且最終客戶的客戶體驗比以前更加理想。」
Manu Thapar
Mastercard 網路和情報 CTO
更多金融服務客戶案例
Total results: 508
找不到任何項目
-
North America
Modernizing Clearing Infrastructure Using AWS Countdown with M1 Finance
M1 Finance, a fintech startup, needed to innovate beyond third-party clearing services. Using AWS Countdown, M1 successfully built and migrated to its own cloud-based clearing solution, enabling greater control and innovation potential. -
United States
Speeding Up Security Forensics by 97.5% Using AWS Step Functions with OneMain Financial
OneMain Financial used AWS Step Functions and Amazon EC2 to build its own digital forensics solution, OneFor(ensics), saving costs and reducing the time between alert and investigation. -
Other
Nomura Uses Llama Models from Meta in Amazon Bedrock to Democratize Generative AI
Learn about Nomura's journey to democratize generative AI firm-wide using Llama models from Meta in Amazon Bedrock. -
Malaysia
Deriv Boosts Productivity and Reduces Onboarding Time by 45% with Amazon Q Business
Deriv, one of the world’s largest online brokers, faced challenges accessing vast amounts of data spread across various platforms. It turned to AWS for help and adopted Amazon Q Business, a generative artificial intelligence (AI) tool, to retrieve and process data from multiple sources. By setting up connections to Slack, Google Docs, Google Drive, Web Crawler, and GitHub, Amazon Q Business quickly became integral across departments, including customer support, marketing, content creation, and recruiting. By leveraging Amazon Q Business, Deriv reduced the time spent onboarding new hires by 45 percent, minimized recruiting task times by 50 percent, and dramatically streamlined campaign management for marketing teams.
開始使用
各行各業各種規模的組織每天都在使用 AWS 來變革其業務和履行其使命。聯絡我們的專家,立即開始您的專屬 AWS 雲端之旅。