A Relevant Customer Identification Algorithm Based on the Internet Financial Platform

In order to improve the intelligent search capabilities of Internet financial customers, this paper proposes a search algorithm for Internet financial data. The proposed algorithm calculates the customers corresponding to the two selected financial platforms based on the candidate customer set selected from the seed dataset and combined with the restored social relationship. Moreover, it also calculates the similarity of each field between the pairs. Furthermore, this article proposes an entity customer classification model based on logistic regression. Through the SNC model, threshold propagation, and random propagation, the model is transformed into an algorithm that identifies the associated customers, eliminates redundant customers, and realizes associated user identification. Experimental results verify that pruning increases the accuracy of identifying related customers by 8.44%. The average sampling accuracy of the entire customer association model is 79%, the lowest accuracy is 40%, and the highest is 1. From the sampling results, the overall recognition effect of the model reaches the expected goal.

Customer Segmentation using Data Mining Techinique

Data mining technique can help implementing customer relationship management dimensions namely customer identification, customer attraction, customer retention and customer development . Various Data mining functions can be used to implement CRM elements . This review paper is focused on one of the element of customer identification that is customer segmentation and its implentation using K-means clustering technique. It discuses in breif about the K-means Algorithm.

Proposal for Customer Identification Service Model Based on Distributed Ledger Technology to Transfer Virtual Assets

Recently, cross-border transfers using blockchain-based virtual assets (cryptocurrency) have been increasing. However, due to the anonymity of blockchain, there is a problem related to money laundering because the virtual asset service providers cannot identify the originators and the beneficiaries. In addition, the international anti-money-laundering organization (the Financial Action Task Force, FATF) has placed anti-money-laundering obligations on virtual asset service providers through anti-money-laundering guidance for virtual assets issued in June 2019. This paper proposes a customer identification service model based on distributed ledger technology (DLT) that enables virtual asset service providers to verify the identity of the originators and beneficiaries.

dSubSign: Classification of Instance-Feature Data Using Discriminative Subgraphs as Class Signatures

Applications like customer identification from their peculiar purchase patterns require class-wise discriminative feature subsets called as class signatures for classification. If the classifiers like KNN, SVM, etc. which require to work with a complete feature set, are applied to such applications, then the entire feature set may introduce errors in the classification. Decision tree classifier generates class-wise prominent feature subsets and hence, can be employed for such applications. However, all of these classifiers fail to model the relationship between features present in vector data. Thus, we propose to model the features and their interrelationships as graphs. Graphs occur naturally in protein molecules, chemical compounds, etc. for which several graph classifiers exist. However, multivariate data do not exhibit the graphs naturally. Thus, the proposed work focuses on (1) modeling multivariate data as graphs and (2) obtaining class-wise prominent subgraph signatures which are then used to train classifiers like SVM for decision making. The proposed method dSubSign can also classify multivariate data with missing values without performing imputation or case deletion. The performance analysis of both real-world and synthetic datasets shows that the accuracy of dSubSign is either higher or comparable to other existing methods.

Blockchain Technology in Payment Systems

The paper is devoted to the analysis of the practice and prospects of application of blockchain technology to provide the exercise of payment services. In particular, it is noted that this technology can be used in the architecture of payment systems, as well as as the technological basis of payment instruments. In addition, blockchain can be used in areas directly related to payment services (for example, for customer identification, currency exchange operations, etc.). The author defines a number of concepts, in particular “peering payment system”, “mining”, “cryptocurrency wallet”. The paper highlights the necessity to differentiate between the actual peering payment systems (networks) and payment services operating with the use of blockchain technology. The latter can be decentralized in terms of how information is transmitted, but not managed. The presence of an entity controlling, administering or otherwise managing individual processes within such a system does not allow it to be regarded as truly decentralized — peering networks are based on the equality of all participants. According to the results of the study, it is concluded that the blockchain technology indeed has a high potential of practical application in the payment field. Meanwhile, currently a cryptocurrency wallet has significant limitations of practical application for the organization of mass payments. Therefore, this technology should not be expected to replace traditional payment institutions in the nearest future. It can be predicted that blockchain will be just implemented into the existing payment infrastructure, rather than replace it completely.

Dynamic Analysis of the Traffic of Vehicles Used in Car Sharing

During digitalization of transport service segment people use modern facilities such as geolocation, M2M connectivity, online payments, customer identification. We can also see a high competition here. The task of car sharing managers is to search for the optimal methods of doing business. As the regulations of business, ecological standards and technology environment of the main players is almost similar because of using the same mobile and cloud resources, it is possible to get a competitive advantage by the development of mobile apps for the employee’s PCs and smartphones of customers. The math model shown in this article is developed for implementing in digital platforms of such kind of business. This gives the opportunity to offer customers more attractive car sharing services and to reduce costs by following the environmental restrictions at the same time.