Observability in Finance allows you to deduce the internal health and status of a system by observing the external data it produces, usually grouped in logs, metrics and traces. We have already dealt with the concept of observability as an evolution of traditional monitoring tools. In other words, monitoring must take place proactively, to avoid that the onset of a problem would lead to the inability to use one or more services. For this reason, organizations must adopt technologies that promote observability to minimize the average resolution time or MTTR (mean time to resolution). In the financial sector, this need is dictated above all by a very demanding request from customers. Since access to banking and financial services, in general, occurs 24 hours a day, 7 days a week, user expectations cannot be disregarded, considering that a bad customer experience affects much more than a positive customer experience on the levels of engagement.
It is not only the end customers who drive the need for observability in Finance. Even from within the financial organizations themselves, requests mainly come from figures such as those of the CFO (Chief Financial Officer), today. As Gartner recently recalled, more and more "stakeholders will require real-time access to financial data and advanced analysis". In other words, a few hours of downtime can cost millions in terms of revenue losses both when a user is unable to operate on their current account - and they finally end moving to another bank -, and when the C-Level of a financial company cannot rely on the data upon which it bases its business decisions. To ensure a customer experience that lives up to expectations and grant constant and uninterrupted access to critical data for business decision-makers, it is necessary to be able to observe what is happening anywhere in a system, no matter how complex its architecture is.
The complexity of observability in Finance comes from the exponential growth of data both in the endpoints and in the basic infrastructure of the financial services architecture. As network hosting moves towards cloud or edge points, the amount of data generated multiplies, and their monitoring is complicated mainly due to the proliferation of components to be observed such as microservices. A heterogeneous environment risks slowing down the detection of incidents before they impact the end-user experience. Log management activities, for example, must be carried out in streaming to be able not only to shorten the timing of diagnosis, but also to introduce remediation mechanisms that anticipate the onset of problems. In the case of observability in support of Finance, therefore, the assessment phase - when the financial institution turns to a partner with expertise in the field - must serve to identify the SLOs (Service level objectives) suitable for monitoring.
The SLOs can lead, for example, to the four processed in the Google SRE (Site Reliability Engineering) method, i.e. latency, traffic, errors and saturation. The important thing is that the identification of the parameters is associated with an effective alert model. Precisely because of the complexity of the environment to be monitored, observability in Finance must select among the indicators those that are really relevant to match the generation of alerts. In addition to ensuring that, there is no decay in the customer experience, observability performs additional security functions compared to any other economic sector. Think of those related to fraud detection which in the context of Fintech must block any behavior deemed anomalous before it is too late. A task that is possible only thanks to the automation that observability systems in Finance can achieve through the use of artificial intelligence. In fact, observability intended as proactive monitoring cannot be conceived without the use of machine learning algorithms applicable to the entire technological stack of the bank or financial institution under observation.