Predictive anomaly detection helps finance improve trading system performance

In the fast-paced world of finance, IT Operations teams that use predictive anomaly detection can stay ahead of performance problems. This became the prime focus for one of the world’s largest and most respected financial institutions. Their adoption of InsightFinder, coupled with Extrahop data, highlights a key way businesses can revolutionize operations and customer satisfaction. In this case study illustrated by the automatically detected anomalous metric data, InsightFinder predicted significant transaction delays in the trading system caused by a network traffic surge leading to high TCP transmission errors. The abnormal network traffic surge was later confirmed by the operator to be a software bug triggered by a corrupted data field in the client order data. 

By assimilating data from varied sources such as Kafka, ExtraHop, Service Now, and application logs from Elastic/Splunk, InsightFinder helped the customer to create holistic views about their critical systems. 

Direct Impact of Predictive Anomaly Detection

  • Full Operational Visibility: With a holistic view of their operating data, operators could see any abnormal client transaction delays even before a client was aware of them. No more reactive measures – operators could proactively address issues, thereby enhancing client satisfaction.
  • Problem Localization: By precisely determining whether an issue originated from the network, the server machines, or the application itself, the operator was able to drastically cut down on MTTR. This translated into reduced downtime and enhanced reliability.
  • Incident Prediction: Financial institutions never want to be caught off-guard, especially with client-related matters. InsightFinder’s ability to raise early warnings was a game-changer. By receiving predictions in advance, the operator could take preventive measures, ensuring client services remain healthy despite network glitches, server failures, or application software bugs.

Customer Service Improvements from Predictive Anomaly Detection

With those unique AI powered predictive capabilities,  insightfinder helps the bank to improve customer service in three main ways: 

  • Reduced SLA Violations: By catching performance problems early, the bank saw a significant reduction in SLA violations, ensuring they met their commitments to clients consistently.
  • Quickly Identify and Resolve Root Cause: With the ability to localize problems, the bank could quickly identify potential root causes, further offering recommendations for areas of improvement. This not only enhanced efficiency but also cut down the time spent diagnosing outages.
  • Streamlined Remediation Process: One of the standout features was the auto-remediation capability. By reducing manual intervention, UBS saw faster resolution time and a decline in associated manual errors.

The strategic integration of Extrahop and InsightFinder highlights the benefits of predictive anomaly detection for the bank. By anticipating problems, ensuring quick resolutions, and maintaining a high standard of client satisfaction, the use case shows how financial institutions can gain competitive advantage from AI powered predictive technology. To learn more about integrating ExtraHop and InsightFinder, go here. Test out InsightFinder by signing up for a free trial.

FAQs about anomaly detection in critical financial applications with InsightFinder:

Q. How does InsightFinder compare to other APM and Observability tools specifically in terms of accuracy and speed of anomaly detection in financial applications?
A. InsightFinder uses machine learning (specifically, unsupervised behavior learning – or UBL) to predict anomalies before they occur. Many customers achieve better than 80% successful prediction rates using predictive anomaly detection. As a result, InsightFinder replaces anomaly detection time with anomaly prediction time, reducing false positive alerts, speeding reaction time, and dramatically increasing overall system reliability.

Q. What are the specific technical requirements for integrating InsightFinder with existing financial systems and databases?
A. InsightFinder integrates with more than eighty data systems – you can find a complete list here. With both SaaS and On-Premise deployment options, InsightFinder can be deployed to incorporate many data types into the prediction models. Technical details can be found here.

Q. Are there any case studies or examples of how InsightFinder has helped other financial institutions besides the one mentioned in the article?
A. Of course! Check out our customer success stories here.

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