Case study – using deep learning for fraud detection

Using ML techniques to identify fraudulent documents is an active and challenging field of research. Researchers are investigating to what extent the pattern recognition power of neural networks can be exploited for this purpose. Instead of manual attribute extractors, raw pixels can be used for several deep learning architectural structures.

Methodology

The technique presented in this section uses a type of neural network architecture called Siamese neural networks, which features two branches that share identical architectures and parameters.

The use of Siamese neural networks to flag fraudulent documents is shown in the following diagram:

Figure 8.17: Siamese neural networks

When a particular document needs to be verified for authenticity, we first classify the document based on its layout and type, and then we compare it against its expected template and pattern. If it deviates beyond a certain...