Identity Fraud Detection with Deep Learning
Machine Learning is a branch of artificial intelligence that allows computers to learn categorized data for the purpose of classifying new, similar data. Derived from Machine Learning, Deep Learning is based on a system of neural networks “like” the human brain. The advantage is that it makes an autonomous learning allowing to accomplish complex tasks.
Nowadays, the fields of application of Machine Learning and Deep Learning are vast. Digital marketing, business intelligence, facial recognition, IT security, voice assistance, fraud detection are examples of use cases for these concepts. The healthcare sector also uses Deep Learning, which reveals its effectiveness in detecting certain types of cancer.
In the context of fraud detection, it will be necessary to move from the classic concept of Machine Learning to deep neural networks such as Deep Learning. Indeed, to efficiently analyze the images of received identity documents, the algorithm analyzes the hierarchy of objects defined by the pixels rather than each pixel separately. The image is scanned by the layers of neurons to bring out the essential characteristics according to the use case. It is enough to superimpose the layers to obtain characteristics more and more relevant.
Deep Learning is proving to be a powerful weapon against identity fraud. The algorithm automatically identifies anomalies on parts likely to be fraudulent. Thanks to Deep Learning, a solution for continuous monitoring of user behavior and their biometric data makes it possible to detect cases of identity theft. The main advantage of Deep Learning in fraud detection is that it allows you to have a unique anti-fraud system adaptable to any type of identity document.