Personal Credit Risk Identification Of Commercial Banks Based On Knowledge Graph Technology

With the advent of the era of big data,in fact,banks have been transformed into big data enterprises.All kinds of business data stored by banks are very important for their own credit risk identification.With the increasing diversification of financial institutions' assets,personal credit asset management is becoming more and more complex.The diversification and complexity of personal credit data make the personal credit management of commercial banks face more and more risks.The traditional customer credit evaluation system is difficult to identify the personal credit risk in time and effectively.This paper introduces the traditional methods of credit risk identification,takes the personal credit data of banks,introduces the technology of knowledge graph,tries to build a small-scale knowledge graph of bank credit risk identification,and directly explores the relevance between various types of data.At the same time,we use the knowledge map to identify and explain the complexity of credit customer relationship,and deeply mine the correlation between various types of customer credit data.Through the knowledge graph,we can visualize the correlation between bank customers,so as to identify the credit risk under the complex relationship.This not only omits the time-consuming and laborious process of traditional manual modeling,but also adapts to the identification of personal credit risk in the complex financial business environment.