| Summary: | 碩士 === 國立臺北科技大學 === 管理學院資訊與財金管理EMBA專班 === 107 === It is difficult to figure out the problem of interrupted transactions between the core banking system and the application systems and solve problems quickly. In this study, Bank A evaluation of big data analysis tools including ELK, Splunk and Hadoop and the selection of Splunk search engine tools were implemented to address the issue of disruption between core banking systems and application sys-tems.
Bank A imported the different types of application server logs and core bank-ing logs into Splunk and associated them to exploit the problems of transaction interruptions, to ensure the services are normal or abnormal? Is the transaction packet lost? When is it lost? The engineers can identify the problems and analyze the problems. After implemented, it significantly reduces the time to identify the problems, and sends the alert to the relevant people before customer finds the transaction problem. It effectively reduces customer complaints, increases customer satisfaction and improves the bank’s goodwill.
This method can be implemented to other banks or conglomerates, that have different brand systems purchased at different times, or several systems developed at different times, and have problem with the transaction integration or the need to alert maintainers.
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