There are many different methods of fraud, including check forging, credit card theft, accounting malpractice, fraudulent insurance claims, and more. Fraud detection is crucial in almost every industry, including finance, banking, government, insurance, and law enforcement.
Since recent years saw an increase in fraudulent activities, sophisticated fraud detection is essential to prevent the loss of hundreds of millions of dollars each year.
Modern fraud detection involves the use of data mining and statistics to:
- Spot fraudulent patterns and anticipate fraud
- Detect fraud at an early stage
- Take immediate action to prevent losses
Spotting patterns and identifying phenomena that are typically associated with fraudulent behavior makes it much easier to detect and prevent fraud.
There are several sophisticated tools to generate predictive models and calculate the probability of fraudulent behavior and estimate the dollar amount of fraud. Data mining tools include, among other things, the following:
- Decision trees
- Machine learning
- Association rules
- Cluster analysis
- Neural networks
Fraud detection software for anomaly protection and data protection include Microsoft Azure and Crayon’s AI. In this article, we will be discussing anomaly detection as a method to prevent fraud and how it is possible with these tools.
Predictive Modeling Problem
An effective way to address fraud detection is to approach it as a predictive modeling problem, which means treating and correctly anticipating fraud as a rare event. The predictive modeling problem involves the use of historical data where fraudulent activity was identified and verified. Add this data to the predictive modeling workflow to increase the chances of identifying the same data in the future.
By treating fraud as a rare event and taking historical data into account, it becomes possible to formulate predictors that, if present, can indicate that fraud or loss of resources is likely.
The presence of predictors or other information can then be used in the rejection of applications or the launch of a detailed investigation into claims.
What if There Is No Historical Data?
In most cases, there is not enough historical data to formulate fraudulent observations. To detect and prevent fraud, organizations have to monitor data continuously and be on the lookout for anomalies or intrusions that can indicate deviations from the norm.
Anomalous data are rare items, observations, or outliers that differ significantly from the majority of data. This data is cause for suspicion. Anomalies can typically be associated with issues like malfunctioning equipment and structural defects. In the medical industry, for example, anomalies are helpful to make diagnoses.
Anomalies can also be used to detect the possibility or occurrence of fraudulent activities.
There are three categories of anomalies:
These are singles instances of data that differs significantly from the rest.
These are abnormalities that are context-specific and are typically noticeable over a time-series and by taking surrounding circumstances into account.
This is a collection of related data instances that are anomalous with respect to an entire data set. In other words, an individual data instance in the group may not be anomalous by itself, but several data instances as a group can indicate an anomaly.
Anomaly detection refers to the method used to identify outliers or unusual patterns that differ from the norm in a complex or dynamic environment.
Anomaly detection differs from novelty detection, which involves the identification of unobserved patterns in new observations. Anomaly detection should also be differentiated from noise removal, which refers to the isolation of unwanted observations to detect a pattern or to come to a conclusion.
One of the objectives with anomaly detection techniques is to predict the likelihood of fraud and to minimize losses. This can happen by rejecting an application for credit or increasing the effectiveness of insurance claims.
Machine Learning Platforms
Since the rise of big data, businesses have more data to their disposal than ever before. The problem is that they don’t necessarily have the means to derive insights from all this information.
When it comes to flagging patterns or identifying information that may be an indication of fraudulent activity, datasets may be too information-rich to manage effectively. Also, organizations may have difficulty using the data to make decisions in real-time.
Machine learning proved to be one of the critical technologies to solve this problem, and organizations are increasingly relying on AI to process data and detect anomalies.
AI solutions perform the following tasks:
- Analyze datasets
- Determine the parameters of normal behavior
- Identify breaches in the normal patterns that may indicate anomalies
AI solutions can be applied to a wide range of applications, and many prominent technology providers started developing them. Microsoft Azure, for example, features the Time Series Anomaly Detection in Machine Learning Studio to detect unusual patterns in time series data.
Fraud detection machine learning solutions are not only able to monitor large data stories, but they also become more intelligent over time. Each time they run, they develop experience and, as a result, the platforms’ responses become more accurate as time goes by.
Do Organizations Need Anomaly Detection Software?
Entities that conduct wide-scale operations and that are particularly susceptible to fraudulent activities can increase their efficiency significantly with anomaly detection platforms.
An organization that cannot interpret data on demand is not in a position to manage data and respond to fast-moving activities, behaviors, or patterns. Due to the nature of cybersecurity threats or fraudulent activities, it is crucial to respond immediately to prevent crime and minimize losses.
Manually analyzing and interpreting hundreds of thousands of figures and statistics per day is not possible. With machine learning, however, an organization has the means to understand data activity in real-time. As a result, it is possible to recognize and respond immediately to an unusual pattern or suspicious activity.
Another reason organizations need anomaly detection software is because it detects small and widespread anomalies that would typically go over a team of human users’ heads, especially if the machine reaches high levels of experience.
Contemporary enterprises that manage substantial qualities of data for anomaly and fraud detection should view anomaly detection systems as a necessity. Without these systems, it may not be possible to react immediately to real-time and accurate information.
While these systems may be sluggish at first, their ability to learn will, over time, provide accurate insights on anomalies and fraudulent activities.
Anomaly Detection with Machine Learning in Azure Stream Analytics
While monitoring data with AI is possible, it was not always easy.
Users had to:
- Be familiar with the use case and problem domain
- Integrate the detection platforms with stream processing mechanisms by using sophisticated data pipeline engineering
- Rely on costly custom machine learning models
These requirements pose a high barrier to entry and, as a result, many entities opted to forgo effective data management and fraud protection.
With Microsoft Azure and Crayon’s AI, service monitoring, usage monitoring, and performance monitoring are quick, easy, and affordable. The software eliminates the need for building complex anomaly detection models and integration with streaming pipelines.
Microsoft Azure and Crayon’s AI use statistical time-series data to detect positive and negative trends and fluctuations, even if the range of values is dynamic. Crayon is a Microsoft approved Azure managed services provider. Among other things, Crayon assists in managing operations and helps to ensure compliance with regulations such as EU laws and GDPR.
The software is built on the following managed services:
- Event Hubs
- Azure Stream Analytics
- Data Factory
- Azure SQL Database
- Machine Learning Studio
- Service Bus
- Application Insights
- Power BI
Event Hubs is a big data streaming platform that can ingest and process millions of data instances per second. Event Hubs transforms and stores data with the use of any real-time provider or storage adapter.
Azure Stream Analytics processes incoming data from devices, sensors, websites, social media, apps, and more that were ingested to Event Hub. To use Stream Analytics for examining data, you have to create a Stream Analytics job that specifies the input source as well as the transformation query.
The transformation query defines how Stream Analytics should look for patterns.
Azure Storage is a Microsoft cloud solution for contemporary data storage scenarios. This service offers the following:
- Azure Blobs: A scalable object store for data objects
- Azure Ques: A reliable messaging store
- Azure files: A manageable file system service
- Azure Tables: A NoSQL store
By default, Data Factory will contact the Anomaly Detection API every fifteen minutes on the data in Azure Storage. It then stores the results in an SQL database.
Azure’s SQL Database stores the following:
- Results from the Anomaly Detection API
- Binary detections
- Detection scores
- Optional metadata
Metadata can be sent with raw data points to allow for more detailed reporting.
Machine Learning Studio
Machine Learning Studio hosts the Anomaly Detection API and allows the user to train, automate, and manage all machine learning models. Machine Learning is a cloud service that enables computers to learn without the need for explicit programming.
Service Bus is a cloud messaging service that allows the user to send information between applications and services. With Service Bus, it is possible to enable consumption by external monitoring services.
Application Insights monitors all your web applications regardless of whether they are hosted in the cloud or on your premises. Application Insights also offers a connection point to several developmental tools. It also integrates with Visual Studio to provide support to DevOps processes.
Power BI features dashboards that display raw data as well as anomalies that Azure detected.
Modern Fraud Analytics
Recent advancements in technology amplified the risk of fraud for companies and their customers significantly.
Failing to detect and prevent fraud results in billions of dollars of loss per year for the financial industry alone. Artificial intelligence with deep learning for fraud detection is rapidly becoming the saving grace for many entities that are susceptible to fraud.
Organizations have access to unlimited data that can provide them with insights relating to anomalies and fraud. Even with the advancements in technology and the access to data, however, detecting anomalies and responding immediately to prevent fraud can be a challenge. This is especially true if an organization doesn’t have the required financial and human resources.
With Microsoft Azure and Crayon’s machine learning and modern fraud analytics, using big data to detect anomalies and fraud is quicker and more cost-effective than ever before. This software is useful in delving deep to find seemingly insignificant anomalies that are difficult to detect but that can result in significant losses.
Its ability to detect suspicious patterns improves after every session as well. This makes it suitable for use by entities with unique requirements over the long run.