Information matching model and multi-angle tracking algorithm for loan loss-linking customers based on the family mobile social-contact big data network |
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| Institution: | 1. Institute of Finance Engineering in School of Management/School of Emergency Management, Jinan University, Guangzhou 510632, China;2. School of Emergency Industry, Guangzhou Pearl-River College of Vocational Technology, Huizhou 516131, China;3. Guangdong Emergency Technology Research Center of Risk Evaluation and Prewarning on Public Network Security, Guangzhou 510632, China;1. College of Computer and Information, Hohai University, Nanjing 211100, PR China;2. School of Computer and Information, Anqing Normal University, Anqing 246133, PR China;3. School of Foreign Languages, Anqing Normal University, Anqing 246133, PR China;4. School of Software Engineering, Jinling Institute of Technology, Nanjing 211169, PR China;1. School of Economics and Management, Harbin Engineering University, Harbin 150001, China;2. Management School, Harbin University of Commerce, Harbin 150028, China;3. Department of Computer Science and Information Engineering, Asia University, Taichung, 41354, Taiwan;4. Department of Computer Science and Engineering, Kyung Hee University, Republic of Korea;1. Department of Information Science and Technology, South China Business College, Guangdong University of Foreign Studies, Guangzhou 510545, China;2. Department of Computer and Information Science, University of Macau, Macau 999078, China;3. Department of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, China |
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| Abstract: | This article focuses on the tracking problem of loan loss-linking customers based on the family mobile social-contact big data network. By defining suspected loan loss-linking customer and family member intimacy, a mobile social-contact big data network of the loan loss-linking customers’ family members is constructed, and similarity of mobile phone usage habits, contact similarity, and contact point similarity in the mobile social-contact big data network are defined and studied accordingly. Then, the similarity matching degrees of mobile phone usage habits, contact locations, and contacts between suspected loan loss-linking customers and loan loss-linking customers are analyzed from the perspective of similarity. This establishes an information matching model of loan loss-linking customers, and proposes the multi-angle tracking algorithm of loan loss-linking customers, allowing information matching and multi-angle tracking for loan loss-linking customers, and applies the model and the algorithm to the loan data of a bank in China. The empirical results show that the proposed model and algorithm can track loan loss-linking customers, and the algorithm exhibits rapid convergence. This study has important significance and practical value for financial institutions to track loan loss-linking customers and recover funds. |
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