Realizing with machine learning customer identity verification and vitality detection in the banking remote customer acquisition

The Remote Customer Acquisition process is a process in which the person does not have any physical transactions (going to the branch, wet signature, showing her original identity, etc.). Users who want to be customers in this process; With ID card and face recognition technologies, they can become bank customers regardless of time and place. This system, which enables to be a customer of a bank in an easy and reliable way, eliminates the necessity of being in a branch and dependent on documents. In this study, it is aimed to carry out the Remote Customer Acquisition process in banks using machine learning. Thus, for remote identification, it will not be necessary to make a video call between the customer and the customer representative after all the steps and confirm the authenticity of the person. The process will be automatically advanced and the reliability of the customer will be decided by conducting an accuracy analysis. For the process of becoming a customer; An application is coded in Python using PyQt. Machine learning algorithms were used in the identification stages on this application. At the same time, accuracy analysis was performed using technologies such as OCR, NLP and OpenCV. The contribution of this study to remote customer acquisition processes, especially in the field of banking and finance, is evaluated.