SOM approach for clustering customers using credit card transactions

Purpose–The purpose of this paper is to generate customer clusters using self-organizing map(SOM)approach,a machine learning technique with a big data set of credit card consumptions.The authors aim to use the consumption patterns of the customers in a period of three months deducted from the credit card transactions,specifically the consumption categories(e.g.food,entertainment,etc.).Design/methodology/approach–The authors use a big data set of almost 40,000 credit card transactions to cluster customers.To deal with the size of the data set and the eliminated the required parametric assumptions the authors use a machine learning technique,SOMs.The variables used are grouped into three as demographical variables,categorical consumption variables and summary consumption variables.The variables are first converted to factors using principal component analysis.Then,the number of clusters is specified by k-means clustering trials.Then,clustering with SOM is conducted by only including the demographical variables and allvariables.Then,a comparisonis made and the significance of the variablesis examined by analysis of variance.Findings–The appropriate number of clusters is found to be 8 using k-means clusters.Then,the differences in categorical consumption levels are investigated between the clusters.However,they have been found to be insignificant,whereas the summary consumption variables are found to be significant between the clusters,as well as the demographical variables.Originality/value–The originality of the study is to incorporate the credit card consumption variables of customers to cluster the bank customers.The authors use a big data set and dealt with it with a machine learning technique to deduct the consumption patterns to generate the clusters.Credit card transactions generate a vast amount of data to deduce valuable information.It is mainly used to detect fraud in the literature.To the best of the authors’knowledge,consumption patterns obtained from credit card transaction are first used for clustering the customers in this study.

Metamorphosis:The Credit Market of Low-Income Households in a Semi-Peripheral Country

In this paper,a theoretical model is developed on the basis of systems theory,which structures the livelihood system of low-income households in a European country characterized by a semi-peripheral economy.Based on the proposed model,the complex system of network connections and formal and informal financial transactions,which households use in their daily lives to cover their expenses,becomes graspable.The proposed theoretical model is analyzed through simulations based on agent-based modelling(ABM)centred on empirical network data.Through the simulations,the author explores the mechanisms of the market and asks what formal and informal credit transactions determine its operation,how these factors shape the local social structure and how resilient the market is to crises.The results show that this dynamic,complex risk-sharing system has an inherent logic and it can mitigate the small liquidity shocks but it is not resistant to bigger financial shocks or overconsumptions.