In simple words: An autoencoder learns to reconstruct; reconstruction error can help flag anomalies.
Two parts
An autoencoder has an encoder that compresses input into a smaller representation and a decoder that tries to reconstruct the original input. Training compares the reconstruction with the original.
Finding unusual data
If trained mainly on normal examples, an autoencoder may reconstruct normal cases well and unusual cases poorly. A large reconstruction error can flag an anomaly for review. It is a signal, not proof of fraud or a diagnosis.
See the flow
input→encoder→compact representation→decoder→reconstruction
Simple example
Train on typical factory sensor readings. If a new reading is rebuilt poorly, a technician can inspect the machine.
Remember: An autoencoder learns to reconstruct; reconstruction error can help flag anomalies.