As fraudulent activities in the financial services sector become progressively more sophisticated, institutions are increasingly turning to various technologies to combat fraud. Fraud detection software for banks spans a range of technologies and capabilities, which offer the potential for dramatic improvement over conventional approaches. Recent advances in various underlying technologies have greatly enhanced the potential accuracy and efficiency of such software, and have expanded the breadth and depth of capabilities in detecting various aspects of fraudulent activity. With the powerful arsenal of this new generation of specialized software, financial institutions can now more effectively arm themselves against the financial losses associated with fraud.
Capabilities of Fraud Detection Tools for Banks
Next-generation fraud detection software may include a number of capabilities. Among these are:
- Real-time Monitoring. Some systems have the ability to continuously monitor transactions and account activities in real time, which allow them to identify and respond to suspicious activities as they happen. This is in contrast to traditional methods that often rely on delayed review and periodic audits. Because of the sheer speed with which these activities occur, the ability to monitor transactions continuously enables institutions to flag those deemed suspicious soon enough to mitigate any resulting fraud.
- Pattern Recognition. Some systems use advanced analytics to identify unusual patterns and deviations from a customer's typical behavior. If a customer, for example, suddenly makes an unusually large withdrawal in a foreign country, the software can flag this as suspicious. With increasing sophistication, activities that might otherwise escape manual scrutiny can be flagged very quickly.
- Anomaly Detection. Many systems attempt to analyze large sets of transaction data at once, in order to identify otherwise obscure anomalies that may indicate fraudulent activity, such as multiple large withdrawals or transactions occurring within a very short time frame.
- Machine Learning. Fraud detection software often employs machine learning (ML) models that improve over time by learning from historical data and user feedback. This enables you to quickly adapt to techniques and trends in fraud as they continue to evolve.
- Rules and Thresholds. In addition to machine learning, many systems use predefined rules and thresholds to flag suspicious activities. For example, if a transaction exceeds a certain amount or deviates from predefined rules, it can trigger an alert. These rules can then be adjusted or refined to adapt to an institution's specific requirements or needs.
Common Uses for Banking Fraud Detection Software
The frauds that financial institutions face today manifest in several different ways, all of which today’s specialized software may be able to detect and help mitigate. Some examples of how these tools can help are:
- Credit Card Fraud Detection. When a credit card transaction appears to be suspicious (e.g., made from an unusual location or for an atypically large amount of money), the system can send an alert to the cardholder or block the transaction altogether until the cardholder confirms its validity.
- Account Takeover Prevention. In this case, the software monitors login activities and detects unauthorized access attempts, such as multiple failed login attempts, and triggers security measures, like two-factor authentication.
- Payment Fraud Identification. Some systems can identify forged checks or unauthorized payments by comparing signatures and verifying the authenticity of documents.
- Identity Theft Alerting. By analyzing customer data and behavior, some systems can flag suspicious changes that might be consistent with the theft of a customer’s identity.
- Money Laundering Detection. Some software solutions can detect patterns consistent with money laundering, such as large and rapid fund transfers between accounts or even more nuanced behaviors or transactions that might otherwise go undetected.
- Phishing Detection. Some systems can help identify phishing attempts by analyzing email communication and URLs to detect malicious links and content.
How Graph Technology Benefits Fraud Detection Tools
Systems that leverage advanced graph technology, in particular, tend to exceed the capabilities of typical fraud detection software for banks. Unlike those systems, graph analytics tools enable banks to identify, visually represent, and explore complex connections and relationships across large data sets. Such tools allow for advanced querying, pattern recognition, and rapid analysis of interconnected data points.
Armed with these graph-specific capabilities, you can streamline your institution’s analytic processes, accelerate your decision-making, and significantly improve your ability to detect fraud, ultimately empowering you to stop fraud faster and mitigate your financial crime risk more effectively than with other types of software.
As a comprehensive financial crime risk platform developed with advanced graph and database technologies, DataWalk offers unique capabilities for detecting and investigating fraud more accurately and efficiently. These capabilities enable you to:
- Streamline Data Preparation. DataWalk accepts your data as-is and allows you to transform the data as needed on your own, without requiring additional work from data owners or IT.
- Integrate and Analyze Internal/External Data. Easily connect all your desired internal data sources (databases, Excel files, data warehouses, images, etc.) within the software, and connect them with key external sources such as subscription services, public records, and social media*.
- Easily Create or Tune Rules and Scores. DataWalk provides a library of rules that can easily be customized and adapted and allows you to generate and optimize your own rules and scores. Unlike “black box” solutions, DataWalk makes all these rules transparent, so you can see which rules drove a score, test your hypotheses, and fine-tune any of them yourself.
- Reduce False Positives and Spot More Suspicious Transactions. By tuning rules and scores, you can continually improve your ability to identify suspicious transactions and significantly reduce your rate of false positives—to as low as 10%.
- Dramatically Accelerate Triage. With DataWalk, you can see instantly which components have the greatest influence on a high-risk score, and then automatically assign that transaction to further analysis or investigation, greatly accelerating the entire triage process. With an accuracy rate as high as 90%, some institutions can eliminate triage altogether.
DataWalk automatically identifies organized crime groups and detects suspicious transactions with up to 90% accuracy, enabling bankers to dramatically reduce false positives and accelerate triage.
- Identify Potential Crime Rings. Through sophisticated graph algorithms, DataWalk can not only identify “clusters” that may represent organized crime groups, but also detect (and alert) when new entities connect to these crime group clusters.
- Expedite Fraud Investigations. DataWalk integrates capabilities for both detection and investigation of suspected fraud, providing you with a single interface for an aggregated view and analysis of all your data. You can create reports, and share data, analyses, and investigation files with authorized colleagues.
- Automate and Operationalize Results. Designed to be part of an enterprise workflow, DataWalk offers open APIs to import/export data from/to other systems and to enable other systems to initiate analyses or extract data or results from DataWalk.
Fraud detection software for banks with graph-specific capabilities, such as those developed by DataWalk, can optimize and accelerate many of the processes underpinning banking fraud detection and investigation. Doing so empowers you to analyze and understand suspicious activities more effectively, uncover hidden patterns and trends, and ultimately mitigate your exposure to fraud risk.
*Accessing social media data requires an additional third-party software product purchased separately.