What are the main challenges of using AI in fraud detection?

What are the main challenges of using AI in fraud detection?

Some key challenges include data quality, model bias, and the evolving nature of fraud tactics.

How does data quality affect AI's performance in detecting fraud?

Poor data quality can lead to inaccurate predictions, making it harder to identify genuine fraud cases.

What can be done to improve data quality for AI models?

Improving data quality involves regular data cleaning, validation, and ensuring diverse data sources.

Can AI adapt to new fraud tactics over time?

Yes, AI can adapt by continuously learning from new data, but it requires regular updates and retraining.

What role does human oversight play in AI fraud detection?

Human oversight is crucial for interpreting AI results, making final decisions, and providing context that AI may miss.