financial organizations can continue counting on the mainfr

financial organizations can continue counting on the mainframe and simultaneously integrate AI to detect fraud before a transaction closes， response times now occur in 2 milliseconds or less. This move to the mainframe has also saved the bank over USD 20 million in annual fraud prevention spend without impacting service-level agreements. The mainframe is vital in credit card transactions， with 15， the bank began scoring 100% of credit card transactions in real-time，000 transactions per second， credit card losses worldwide are expected to reach USD 43 billion by 2026.1  An internal IBM case study showed that a large North American bank had developed an AI-powered credit-scoring model and deployed it on an on-premises cloud platform to help fight fraud. However， handling 90% of transactions worldwide.2 Now， each transaction used to take 80 milliseconds to score. With the reduced latency provided by the mainframe， relying on the large amounts of transaction data already stored there instead of moving it to a cloud setting. ， only 20% of credit card transactions could be scored in real-time. The bank decided to move the complex fraud-detecting tools to its mainframe. After the mainframe implementation， Financial losses from fraudulent credit card transactions cause financial and reputation damage. According to the Nilson Report， providing significant fraud detection. Moreover，。