What are the main challenges in AI fraud detection?

What are the main challenges in AI fraud detection?

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

How does data quality affect fraud detection?

Poor data quality can lead to inaccurate predictions, making it harder to identify fraudulent activities.

What can be done to improve model bias?

Regularly updating training data and using diverse datasets can help reduce model bias.

Are there specific industries more affected by fraud?

Yes, industries like finance, e-commerce, and insurance often face higher fraud risks.

What technologies are being used to combat fraud?

Technologies like machine learning, blockchain, and advanced analytics are increasingly used to combat fraud.