Fraud Prevention

Fraud Detection Your Organization Needs

Financial crimes are becoming increasingly complex, especially with the rise of tactics like synthetic identity fraud. Rules-only systems are unable to keep up with new methods of fraud, often burdening organizations with an influx of false positives.

As a modern monitoring solution, WatchDOG Fraud combines out of the box rules with artificial intelligence (AI) and machine learning to detect fraud in real time, identifying suspicious activity while continuously learning and adapting to investigators’ decisions to perform more efficiently.

What is Fraud Detection?

Fraud detection is the process of monitoring transactions or payments to identify and prevent unauthorized financial activity. A common method of fraud detection involves understanding behaviors and identifying anomalous activities. Monitoring for fraud should be performed across all channels to detect suspicious patterns and can include using transaction data that can be augmented by non-monetary data such as IP addresses, device ID and more. As fraud methods evolve, organizations should deploy a modern solution that can adapt and detect new tactics.

Leverage Real-Time Fraud Detection

WatchDOG Fraud combines new technology with existing rules to modernize and streamline fraud detection and prevention, creating a balanced approach that reduces organizational risk by alerting of suspicious transactions.

Using AI and machine learning, WatchDOG Fraud analyzes behavior patterns to generate alerts when customer behavior deviates from expected patterns. This modern solution minimizes the burden of false positives by adapting to reasoning from closed cases and optimizing rules that reduce the overall number of alerts and cases to investigate.

WatchDOG Fraud Features

WatchDOG Fraud Features

Digital payment channels allow for new points of compromise that require 24/7 monitoring and are especially vulnerable to fraud. WatchDOG Fraud’s AI and machine learning models learn customer transaction patterns and methods to accurately alert on suspicious activity in real time.

Additional WatchDOG Fraud features include:

• False Positive Reduction: AI models reduce false positives and use context and decisioning from investigators and customer behaviors to optimize results and automatically close cases, providing detailed decisioning for auditing and process transparency.
• Pattern Detection: Machine learning models detect activity and generate alerts in real time to effectively monitor transactions. This includes known behaviors such as chargebacks, card-not-present fraud, scams and account testing schemes.
• Synthetic Identity Fraud Prevention: Detect and prevent synthetic ID fraud using AI modeling through IBM’s customizable synthetic data generation tool, which identifies user logins using IP addresses and biometric features to authenticate and identify bad actors.
• Real-Time Fraud Detection: Monitor transactions for fraudulent patterns across all channels and payment methods, emphasizing transactional data that can be augmented with non-monetary identification data to flag deviations from expected behaviors.
• Customer Risk Rating: Dynamically score customer risk using data from internal and external sources to build a Customer Risk Rating, profiling customers who present a higher risk for fraud based on three risk categories:
  • Profile and relationships
  • Services, activities and behaviors
  • Geographic risk
• Explainable Decisioning for Auditors: Allow for direct access to all systems required by auditors and deliver explainable decisioning and full transaction audit trail of all accounts, as well as a dashboard with top-down insight on monitoring activity.