AI plays a crucial role in detecting and preventing financial fraud, offering several improvements over traditional methods:

1. Real-time Analysis: Unlike traditional methods that often involve periodic checks, AI can analyze transactions in real-time, enabling immediate detection and prevention of fraudulent activity.
2. Pattern Recognition: AI can identify patterns and correlations in vast amounts of data that would be impossible for humans to detect. This can uncover complex fraud schemes that might be missed by traditional methods.
3. Anomaly Detection: AI can learn the 'normal' behavior of a user or system and alert when anomalous behavior, indicative of potential fraud, is detected.
4. Predictive Analytics: AI can analyze historical fraud data to predict future fraudulent activities, enabling proactive fraud prevention.
5. Machine Learning: Machine learning algorithms can improve over time, learning from new instances of fraud to enhance their detection capabilities.
6. Data Integration: AI can analyze and cross-reference data from a wide range of sources (e.g., customer transaction data, social media data, etc.) to provide a more holistic view of potential fraud risk.
7. Risk Scoring: AI can assign risk scores to transactions based on their likelihood of being fraudulent, helping to prioritize fraud investigations.
8. Natural Language Processing (NLP): AI can analyze text, such as customer complaints or online reviews, to identify potential fraud.
9. Biometric Authentication: AI technologies like facial recognition or fingerprint scanning can be used for biometric authentication, making it harder for fraudsters to gain unauthorized access to accounts.
10. Automation: AI can automate routine fraud detection tasks, freeing up human investigators to focus on more complex cases.

While AI offers significant improvements over traditional fraud detection methods, it's important to remember that it's not a panacea. AI systems require high-quality data and careful management, and their use also raises privacy and ethical considerations. Additionally, as AI systems become more sophisticated, so too do the tactics of fraudsters, making it essential for AI systems to continually learn and adapt.