7 KYC fraud detection using deep learning
This chapter covers
- Understanding KYC (Know Your Customer) in the digital realm
- Architecting a KYC fraud detection system with automatable checks
- Matching faces across selfies and IDs (Identity Documents) using deep learning
- Building an ID information extraction model using PyTorch and HuggingFace
KYC is a process used by businesses (especially financial institutions) to verify the identity of their customers. You have probably gone through this process while signing up for digital banking apps, trading apps, gambling apps, or even dating apps, where you had to capture a photo of your face (selfie) and a photo of your ID (passport). The purpose of KYC is to prevent bad players (customers) from onboarding, preventing downstream fraud, as an effort to combat the multi-trillion-dollar financial crime industry (https://www.dowjones.com/professional/risk/resources/glossary).
KYC has its origins in the U.S. Bank Secrecy Act of 1970, which required financial organizations to build systems to detect suspicious activity. This matured into a robust formulation of KYC guidelines in the early 1990s by the Bank of England. Events such as 9/11 and the financial crisis of 2008 led to further strengthening of KYC processes (https://dojah.io/blog/the-history-of-kyc).
KYC is typically broken down into 3 key components as shown in figure 7.1: