Detecting unusual occurrences, changes, or shifts in datasets is one technique to handle data efficiently. Anomaly detection is the process of identifying things or occurrences that do not follow a predictable pattern. Anomalies if undetected then turn out into issues like structural flaws, errors, or frauds.
In a wide range of application fields, such as fraud detection, network traffic management, predictive healthcare, energy monitoring, order management, and many others, detecting aberrant patterns in data can lead to important actionable insights.
However, precisely recognizing anomalies might be difficult. What defines an anomaly is evolving, and abnormal patterns are unanticipated. Different domains employ anomaly detection to take actionable insights. We have discussed real-world examples of anomaly detection in various areas.
Anomaly detection in Banking Industry
The anomaly detection approach for transaction data would ask the customer to verify details or go through additional verification processes if a transaction appears suspicious and potentially fraudulent. Anomaly detection can be used to discover technical breakdowns, glitches.
Example:
Fraud Detection: Danske Bank Fights Fraud with Deep Learning
Danske Bank(1), Denmark’s largest bank, teamed up to develop a deep learning-based fraud detection system.
Earlier, Danske Bank’s prior rules-based fraud detection system only had a 40% success rate and created 1,200 false positives each day. Furthermore, the bank found that 99.5 percent of suspicious cases it investigated were not fraudulent. These ineffective inquiries eat up time and resources that may be better spent fighting true fraud.
Danske Bank collaborated to develop a deep learning technology that improved fraud detection by 50% while lowering false positives by 60%. Many choices were likewise automated, but some situations were routed to human analysts for further review.
The system detects anomalies using a ‘champion/challenger’ methodology, according to the case study. To increase accuracy, each model (challenger) learns transaction characteristics that are indicative of fraud and is fed additional data such as customer location. When a model defeats other models, it is dubbed “champion” and is tasked with training more models. This process of improvement is most likely to continue.
Anomaly detection in CPG
Products that fail to meet quality requirements are a waste of resources, money, and effort. If they make it to distribution, though, they pose a serious threat to the company’s image. The value of today’s leading consumer packaged goods firms is driven by consistency and quality.
Example:
Anomaly detection is all about making better decisions by automating data sets with high velocity and volume. A CPG firm that used anomaly detection for demand planning saw a better forecast than their current solution 75% of the time.
When there is an unexpected demand for products, firms can forecast it by anomaly detection, thus preventing wasteful stock storage.
Anomaly detection in Telecom Industry
The telecom industry is undergoing a tremendous transition as a result of the introduction of 5G and the growing popularity of OTT media services.
It’s difficult to keep track of difficulties across multiple dimensions, such as program versions, geographies, system combinations, and so on. Furthermore, for accurate and quick responses, you must swiftly determine the root cause of errors or glitches.
Example:
A huge amount of log files were generated from different sources. Handling massive data in real-time is difficult due to network challenges for the Telecom company. GeakMinds helped a Fortune 500 Company to handle a Big data problem of managing huge amounts of log files from different sources and analyzing issues in the logs. Using anomaly detection, issues were identified in CDN Servers thus handling the big data problem in real-time. This helped them fix issues in their CDN Network and improved customer satisfaction.
Anomaly detection in Healthcare Industry
Anomaly detection algorithms have been used by healthcare providers all around the world to anticipate heart attacks, strokes, sepsis, and other dangerous problems by utilizing tools derived from machine learning models.
Example:
Thousands of electronic entries linked to patient illnesses and treatments are now included in inpatient medical records. The goal of this study is to create tools that can detect anomalous patient-management trends using previously obtained patient data. By the research funded by grants from the National Library of Medicine, and a grant from the National Science Foundation, Statistical anomaly detection was used as a potential tool for spotting abnormal events that could indicate a medical error or unexpected clinical effects. The method has an advantage over traditional error detection systems. 100 patient cases to assess the performance of our anomaly detection methods were taken and the panel classified 23 of the hospitalization choices as unusual, while the rest 77 were deemed normal.
With the above-stated examples, anomaly detection can be used with any domain involved with data generation. Anomaly detection identifies occurrences that do not follow a predictable pattern which is normally difficult to detect by a human expert. In a wide range of application areas, detecting anomalies in data can lead businesses to reduce errors and thus take actionable insights.