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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.