Fraud Detection In Financial Transactions
Use Case Family:
Anomaly Detection
Business Domain:
Finance
Processes:
Fraud Detection & Financial Transaction Monitoring
Challenge:
Companies struggle to detect fraudulent financial transactions in real time. Manual reviews are time-consuming and error-prone, while traditional rule-based systems often fail to adapt to new fraud patterns. Financial fraud can result in significant losses and damage a company’s reputation.
Solution:
An AI-powered system continuously monitors financial transactions, using anomaly detection and machine learning to identify suspicious activities like duplicate payments or unauthorized transactions. The AI adapts to new fraud techniques and improves accuracy over time. Suspicious activities are flagged for further review.
Benefits:
Early fraud detection protects companies from financial losses and reduces compliance risks. Automation significantly cuts down manual review efforts, allowing employees to focus on more complex investigations. Adaptive algorithms continuously enhance fraud detection while reducing false positives.
Target Group:
Potential Industries:
No Risk systems
No transparency obligantions
Disclaimer
The information provided regarding the risk assessment is without guarantee. The complete classification of a use case according to the EU AI Act depends on numerous regulatory and company-specific factors. Therefore, the risk assessment is always case-specific. The risk assessment logic of Casebase is used for this purpose.