We developed a method for the evaluation and selection of customer business analysis in two stages. First, using the bank’s existing expert model, artificial rules of thumb were used to evaluate the value of each field of the data and establish screening rules. Secondly, the machine learning feature screening method was applied based on the customer’s transaction data to find out whether the customer’s contribution to the bank had a significant impact as a feature of the model. Based on the results, the best classification model was selected through data verification. The effectiveness of the proposed model was validated through actual case analysis, taking wealth management in banks as an example. The classification method, using support vector machines (SVMs), effectively assists banks in identifying potential customers efficiently and in planning to manage customers. This method helps to avoid the traditional blind spots, which emerge based on subjective judgment, and allows bank wealth managers to promote customer relationship management (CRM).
Loading....