Behavioral rules of bank’s point-of-sale for segments description and scoring prediction

摘要

One of the important factors for the success of a bank industry is to monitor their customers & apos; behavior and their point-of-sale (POS). The bank needs to know its merchants & apos; behavior to findinteresting ones to attract more transactions which results in the growth of its income andassets. The recency, frequency and monetary (RFM) analysis is a famous approach forextracting behavior of customers and is a basis for marketing and customer relationshipmanagement (CRM), but it is not aligned enough for banking context. Introducing RF*M* inthis article results in a better understanding of groups of merchants. Another artifact of RF*M*is RF*M* scoring which is applied in two ways, preprocessing the POSs and assigningbehavioral meaningful labels to the merchants’ segments. The class labels and the RF*M*parameters are entered into a rule-based classification algorithm to achieve descriptive rules ofthe clusters. These descriptive rules outlined the boundaries of RF*M* parameters for eachcluster. Since the rules are generated by a classification algorithm, they can also be applied forpredicting the behavioral label and scoring of the upcoming POSs. These rules are calledbehavioral rules.