What are the measures to control the operation and maintenance risks of bank IT systems?

In the digital age, with the rapid development of banking business, the number and deployment scale of computer systems have increased rapidly. With the micro-service of application systems, the relationship between systems has become more complicated, which correspondingly puts forward requirements and difficulties for operation and maintenance systems. Although a perfect monitoring system has been established in the bank, it is very difficult to locate and solve the problem in the face of a million-level alarm storm, which is not conducive to the safe, sustained and stable operation of the system.

In the digital transformation, user-centered is the core foundation to drive the financial industry. Therefore, for banks, securities companies and other financial industries with massive operation and maintenance data, intelligent operation and maintenance is imperative. Adopting advanced operation and maintenance means (intelligent operation and maintenance) is the source of power for enterprises to keep moving forward.

Let's talk about a customer we are serving. The client is a commercial bank.

This commercial bank has built an intelligent operation and maintenance data analysis system through the Sherlock AIOps solution provided by Qingchuang Technology, and collected and analyzed the operation and maintenance data of more than ten systems, including application system logs, alarms, performance indicators, transaction indicators, network performance indicators, etc. The machine learning algorithm is used to realize index anomaly detection, correlation analysis and alarm convergence, thus speeding up the efficiency of problem location and ensuring the system operation. In order to effectively improve the monitoring of abnormal situations, predict future trends, and find hidden dangers in the system in advance, commercial banks have created Sherlock AI Lab, trained and generated a variety of algorithms based on business scenarios, realized the single-index anomaly detection of the system, and greatly reduced the probability of system failure.

At the same time, commercial banks also use Qingchuang Shylock index analysis center and alarm analysis center to realize multi-dimensional index correlation analysis, help quickly find and locate system problems, and improve the efficiency of troubleshooting; Realize alarm convergence, reduce alarm storm and speed up positioning time. At present, the alarm compression rate has reached more than 80%, and the alarm processing efficiency of operation and maintenance personnel has been significantly improved. IT realizes the intelligence of IT system operation and maintenance, and improves the strong guarantee for the healthy operation of business.

In fact, Qingchuang Technology has served many bank customers such as China UnionPay, Bank of Communications, Shanghai Pudong Development Bank and Bank of Ningbo before, helping them to build an intelligent operation and maintenance platform and improve the efficiency of customer operation and maintenance. At present, many projects have entered the second and third stages of construction.