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Anomaly Detection: Identifying Critical Issues to Reduce Revenue Losses - Part 2

Pınar Ersoy
Pınar Ersoy

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    Anomaly Detection Application Areas

    Artificial intelligence techniques have effectively automated hardware and software processes through computerized systems and automation technology. They have also helped deduce meaning from massive volumes of domain-generated textual and quantitative data.

    Furthermore, machine learning has evolved to take a proactive role in decision-making assistance by developing tools that use descriptions and knowledge discovery to anticipate possible results. Anomaly detection is utilized in several sectors in addition to the most prevalent ones, as in the following.

    Digital Analytics

    Example Case Study: Log anomaly detection

    Log data saves essential system operational data. Digital platform solutions need to pinpoint and distinguish computer system failures. Each data center processes massive lines of log records per day. Traditionally, companies would manually analyze logs using keyword search and rule matching. However, the expansion of log volume has rendered proper evaluation ineffective. 

    For this reason, it is crucial to investigate anomaly detection using log analysis. The log messages are gathered from real-world applications. Organizations must employ artificial intelligence techniques to evaluate log data from unsuccessful runs to assist companies in performing efficient problem diagnosis.

    Finance 

    Example Case Study: Detection of excessive amounts of money transfer via cards or bank accounts

    In the domain of payment and finance, fraud detection is crucial. Recent mainstream banking firms utilize real-time outlier detection in audits to stop losses in their tracks. To keep ahead of cybercriminals, organizations may either purchase whole anomaly detection systems or build them from scratch.

    An enterprise requires a sophisticated anomaly detection system that uses ML (machine learning) to monitor and correlate several complex indicators with variable degrees of variation while poring over millions of data points per second to maintain real-time reactivity. Many members of the internal audit team and the financial department have limited expertise in statistical skill sets, including process and data mining and tabular analytics. Furthermore, they might be unable to track, analyze, or identify information vulnerability indicators.

    An anomaly detection system for cards

    Figure 1: An anomaly detection system for cards (Source)

    Cybersecurity 

    Example Case Study: Intrusion detection 

    Recent years have seen a significant increase in the interest in identifying abnormalities across a wide range of security domains. The number of network intrusions is often a relatively tiny percentage of all network activity in network security. While abnormalities are uncommon, they are increasingly essential in these cases related to other events, making it crucial to identify them.

    A network-based anomaly detection system

    Figure 2: A network-based anomaly detection system (Source)

    Insurance 

    Example Case Study: Detection of false insurer activity

    In the healthcare system, insurance-based anomalous activity is widespread and has cost businesses significant amounts of money in payouts to criminals. Health insurers must detect false statements to guarantee that no payments are made to false identities. In the last decade, many companies have invested significantly in artificial intelligence to develop machine learning models to recognize insurance fraud.

    Medical and insurance companies may create supervised, unsupervised, and semi-supervised models to lower the possibility of fraudulent activity for every request received using algorithms and outlier detection tools.

    An insurance-based anomaly detection system

    Figure 3: An insurance-based anomaly detection system (Source)

    Manufacturing 

    Example Case Study: Diagnosis of the problematic unit 

    Various organizations continually use deep learning models to monitor manufactured components’ sensor data. Experts identify and fix any problematic units as they arise as the model evaluates new information. 

    Several top producers are beginning to adopt neural network models, since manually inspecting for errors and abnormalities can cost them more time and cash. By employing a machine learning approach, businesses may track and immediately identify any unexpected events utilizing sensor data from manufactured units.

    A manufacturing anomaly detection system

    Figure 4: A manufacturing anomaly detection system (Source)

    Key Takeaways

    The data collected by enterprises today is more significant than ever. Companies shall be able to observe behaviors, identify abnormalities, and prevent substantial company losses, including malfunctioning infrastructure, fraud, and faults.

    Organizations may gain valuable insights, improve efficiency, and compete better in the information age by spotting abnormalities in statistics. Corporations may employ model-based machine learning with advanced analytics technology to define and track user input and identify unusual activity for improved business impacts. Anomaly detection will likely continue to gain acceptance in various usage areas, such as security and clinical applications.

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