Basics of Fraud Detection Analytics

Risks often accompany technological advances, and online banking is no exception. When bank customers can access their account information through online portals, apps, or call centers, it's more challenging to ensure accurate identification. As a result, fraud becomes easier. Data analysis helps companies detect fraudulent activity before it is discovered by the customer to help identify a criminal transaction and stop it before money changes hands. This article explains the importance of fraud detection analysis and shows how to build a robust fraud detection tool to protect your customers without compromising the digital banking experience.

What is the Purpose of Fraud Analysis?

Fraud Analytics is the process of analyzing data to detect fraudulent activity. Organizations use this information to prevent future fraud and minimize losses from previous fraud.

Fraud analytics relies on two types of data: historical transaction data and customer profile data. Fraud analysts use this information to build models that identify unusual or suspicious behavior. These models help analysts flag potentially fraudulent activity to investigate further.

Organizations use fraud analytics to:

• Prevent future fraud: By identifying trends in fraudulent behavior, organizations can take steps to prevent future attacks.
• Minimize losses: By detecting fraud early, organizations can minimize the financial impact of fraudulent activity.
• Improve customer experience: By reducing false positives, fraud analytics can help improve the customer experience by reducing the number of disruptions to legitimate transactions.

Importance

Fraud detection and prevention analysis are essential in controlling and subverting fraudulent actions. With the constant improvement of technology and methodology, fraud detection and prevention have become increasingly successful. As a result, evolving fraudulent activities become easier to detect and less fruitful for the criminals who try to use them.

Benefits of Fraud Analytics

Organizations gain valuable benefits with the use of fraud analytics. Depending on the system you use, fraud analytics can identify patterns of fraudulent transactions, enhance security measures, and integrate data systems. For financial organizations, the company can identify fraud-vulnerable employees, reliably detect fraud, and decrease costs through organization control measures that ensure system integrity. Reduced fraud exposure increases customer trust, allowing your organization to attract and retain customers.

What Type of Analytics is Used to Detect Fraud?

As a financial institution, you need a risk management plan that detects fraud in real-time. By using different types of analytics for fraud, you can integrate systems and recognize unusual behavior in various ways. These are the most commonly used types of analytics used to detect fraud. 

• Analytics Method: When data is applied that establishes normal behavior, analytics can use statistical calculations to show anomalies and their severity. Suspicious behavior is either identified through anomaly patterns or calculations of maximum and minimum values to establish outliers.
• Sampling: A section of data is tested to establish whether fraud exists by using specific parameters. It offers one of the fastest tests for fraud, but holds the potential risk of missing anomalies in untested data.
• Continuous Monitoring: This method tests all transactions with the use of developed scripts or algorithms that sift through huge volumes of data. The continuous analysis allows the detection of fraudulent activities as they occur.
• Ad-Hoc: With the use of if/then statements this method identifies fraudulent processes using hypotheses. When transactions are conducted, a hypothesis is formulated. The results will indicate whether further investigation is needed,

How Predictive Analytics Supports Anti-Fraud?

Your historical data carries a significant amount of information that can help you identify what is most likely to occur in the future. Using analytical tools, you can create predictive models with specific risk scores to define what actions will be considered legitimate. A risk score is applied when the system recognizes certain behaviors that fall outside the normal range. If the score is outside the accepted range, the transaction in question will be denied. It takes certain steps to apply the process effectively.

• Label Your Historical Database: With a sample or a complete log of historical data, classify actions as legitimate or suspicious.
• Analyze Variables: Not all suspicious behavior is dangerous or representative of fraud. Disregard information that doesn't serve as a meaningful indicator.
• Set Rules: Identify risky actions and behaviors to be flagged as suspicious and set up risk scores with relevant rules.
• Test the Model: Use filters to discover system transactions that fit your criteria.
• Continued Feedback: Fraud methods are continually changing and your model can become outdated. Monitor results and make adjustments to eliminate false positives and recognize new threats.

Machine Learning for Fraud Prevention

When properly optimized, systems that use artificial intelligence can be used to recognize and flag suspicious behavior. Machine learning (ML) is a subset of artificial intelligence that uses data science and data mining techniques as well as input from data scientists to automate fraud prevention. Machine learning techniques can include the use of clustering analysis or machine learning algorithms that identify relationships between data sets and use them to create a baseline of normal behavior. When actions fall outside the baseline, they're flagged as suspicious behavior to be scored for fraud detection.

Can Data Analytics Detect Fraud?

Like most security measures, data analytics tools alone can't detect all instances of fraud. However, the tools can act as early detection systems to recognize questionable behavior and deny potentially fraudulent transactions. No matter what tools you use, data analysis will only be as good as the data supplied to the system. When utilized properly and combined with feedback from data scientists, data analytics can detect fraud through behavioral signals and automatically deny fraudulent transactions.

How to Use Data Analytics Techniques for Fraud Detection

Fraud detection techniques should always be used in an environment designed to protect personally identifiable information (PII). By applying predictive analysis to document verification, it's possible to identify users who are most likely to commit fraud in the future. There is no one-size-fits-all fraud detection system, and most organizations benefit from a layered approach that uses multiple data analytics methods for fraud detection.

Unsupervised Learning (Descriptive Analytics)

When using data sets to quantify a description of normal behavior, descriptive analytics use statistics to automatically discover outliers likely to represent fraud. For instance, clustering algorithms group behavior sets by similarities and differences. Fraud can be detected by various data points that human analysts don't recognize because it doesn't fit within available data supplied to analysts. 

Social Network Analysis and Fraud Graphs

An organizational network includes on-premise devices, remote devices used by employees, cloud applications, and IoT devices that communicate with the network. Similarly, organized criminal gangs have operational connections through a network of devices that can be connected together through network and device locations. 

Social network analysis looks for user connections within your system, based on data points to identify legitimate behavior. It also maps connections outside of your network to trace connections between suspicious actions that indicate fraud. When internal connections are ruled out and suspicious data points are recognized, the information can be used to automate alerts related to these suspicious external data points. 

Technology provides companies in all industries with new ways to improve customer experiences and provide better services. However, there will always be criminals prepared to exploit new technologies for financial gain. In the financial sector, fraud can prove particularly disastrous for customers. To serve your customers properly, it's essential to take responsibility for the potential for fraud in the industry. With the right approach, fraud analytics will provide an efficient level of fraud detection and prevention that can't be matched by human analysts. As a company that helps financial organizations thrive in the digital era, we understand the importance of maintaining a network secure from fraud. To find out how we can help you accelerate your growth, schedule a demo today.