Using ML to Proactively Responding to IT Issues & Protecting Against Fraud

Proactively Responding to IT Issues

IT operations have always been complicated because of the array of different network devices, servers, applications, storage systems, endpoints, and so on. Each system has its unique ways of managing its components. As new versions of software are implemented, configuration updates may be necessary to keep the system running as expected. This is the normal way that systems need to interact in order to maintain a steady state. Often a single mistake in one area can lead to a massive outage, which can be difficult to determine the original cause of a problem - despite the fact that there is significant instrumentation within the data center.

A typical organization might deploy a dozen different monitoring tools to try to keep track of the health if its systems. These monitoring tools capture a huge amount of data about the systems they are monitoring. However, a key challenge is interpreting the large volume of system data and the fact that the data is contained in logs. To understand the data, the logs must be understood. In addition to this log and system data, valuable data can also be found in trouble tickets that include text describing a problem or data from application performance management systems.

Applying machine learning algorithms to this complex IT operations data allows organizations to proactively respond to potential IT issues. Traditionally, event correlation has been used to look for patterns in performance data. There are times, however, when correlation alone might be misleading. Therefore, to gain better accuracy, data scientists are beginning to cluster machine learning algorithms to identify event anomalies. The value of applying machine learning is that it can create a model based on a complex set of data created within the data center including alerts, logs, and instrumentation or sensors. The machine learning algorithm creates a model based on all the relevant data. The model can understand the dependencies between the various elements that comprise the environment. The model can also help identify patterns for ideal performance metrics and compare that to the current state of the environment. As more data is added, the model can be continuously updated.

Protecting Against Fraud

Detecting fraud is a cat and mouse game. Bad actors are becoming increasingly sophisticated in perpetrating fraud. As more and more customers use online services, the potential for fraud has increased dramatically. In addition, payment processors want to make sure that customers have a friction-free transaction and do not want to block legitimate payments. Many companies are finding that the only approach that can help stop fraud is to use software, based on machine learning algorithms. A trained model can identify an anomaly before a fraud event is perpetrated. In essence, the model can identify an action that’s associated with an intrusion or an unauthorized action and block the intruder before damage can occur.

An organization won’t use neural networks or deep learning in isolation. Instead, it will use all three techniques together in order to perform ensemble modeling, which has its advantages. For example, while the linear algorithm might miss some fraudulent activity, it may be very good at catching the most common and straightforward schemes. The final model will take votes from each machine learning model and either approve or block a transaction. This sort of assessment is very similar to a medical patient getting multiple doctors’ opinions. In the end, the goal is that the multiple opinions will yield more accurate results.