Ihor Korol

Convenient and accurate verification of the user of a car sharing system is one of the key components of the successful functioning of the car sharing system as a whole. The machine learning-based KYC (Know your customer) process algorithm makes it possible to improve the accuracy of customer data validation and verification. This makes it possible to eliminate possible losses and reputational losses of the company in case of unforeseen situations while using the client's car sharing services. The object of this study is to find a solution to the problem of user verification in a car sharing system based on the KYC process using deep learning methods with a combination of OCR (Optical Character Recognition) methods. The statement of the user verification problem in the car sharing system was formalized and the key parameters for the KYC process have been determined. The algorithm of the KYC process was constructed. The algorithm includes six successive stages: separating a face in the photograph, comparing faces, checking documents and their validity period, establishing and recognizing ROI (region of interest), formulating a verification decision. To separate the face in the client's photograph and compare faces, methods based on deep learning, as well as the quick HoG method (Histogram of oriented gradients), were considered and implemented. Verification of these methods on a test dataset, which includes images of documents of two thousand clients, showed that the recognition accuracy was 91 % according to Jaccard's metric. The average time of face separation using the HoG method was 0.2 seconds and when using trained models – 3.3 seconds. Using a combination of ROI and ORC separation methods makes it possible to significantly improve the accuracy of verification. The proposed client verification algorithm is implemented as an API on an ML server and integrated into the car sharing system.