为加深对银行客户的洞察，提升银行营销获客与风险管控能力，广发银行基于Hadoop大数据平台，通过Hive on Spark、图计算进行数据加工，结合LFM社群发现、增强决策树等机器学习算法构建了银行客户社交关系模型，挖掘出银行客户社交关系圈，并应用于银行实际业务中。银行客户社交关系圈全面的反映了银行个人客户资金、社交等关系，以全新的视角实现银行对客户洞察从点到面、从单客到客群的扩展，填补银行个人客户社交关系研究与应用的空白。
CGB conducts data processing through Hive on Spark and graph computing in order to deepen its understanding of customers and enhance its ability of marketing and risk control. Combined with LFM community discovery, enhanced decision trees, and other machine learning algorithms, CGB creates social relation model for its customers, discovers their social relationship circle, and then applies this discovery to the bank’s real businesses. A customer’s social relations circle fully reflects their personal funds and social and other relationships and builds customer insights from a single customer to customer groups, filling in the gap between research on customers’ social relations and its application in the real world.
黄文宇 outlines CGB’s procedure for processing its bank customers’ social relation circles: First, CGB integrated and processed a year of transaction logs and other relation data about nearly 60 million existing customers and 100 million potential customers, extracting four types of relationships: capital, social, media, and location. Secondly, the bank used the LFM-based community segmentation algorithm, designed the weight, conductivity, attributes, and other matrices, and identified and mined a customer’s individual relation circle and group circle. Finally, CGB used regression algorithms to discover customer patterns, classify customer groups, and apply and promote their uses in the bank’s retail business scenarios.
Based on customers’ capital relation circles, currently GCB is launching and promoting services from dunning lost customers in credit card and online finance services to precision marketing. The touch rate is 51.57% for dunning to lost customers (of which the customer financial relation circle as the only source of connect information can be 10.07% at the highest proportion) and is expected to bring an increase of 9 million RMB dunning payback each year. The marketing success rate of credit card installment service increased by 15.67% (business income increased by 1.89 million RMB) and is expected to increase the annual income of installment payments by about 45 million RMB.
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