Detecting Fraud Without Historical Data
Detecting fraud without historical data is a complex but achievable task using advanced machine-learning techniques. As fraud schemes evolve, traditional datasets may no longer suffice for detection. The use of unsupervised machine learning models, such as the isolation forest and local outlier factor, allows fraud examiners to identify patterns and anomalies without needing pre-existing data. This approach, along with ensemble models that combine different techniques, offers promising results for detecting fraud in increasingly sophisticated environments. Embracing these tools helps fraud fighters stay ahead of evolving threats and safeguard organizations effectively.