Identifying highly-valued bank customers with current accounts based on the frequency and amount of transactions.

We hypothesize that highly-valued bank customers with current accounts can be identified by a high frequency of transactions in large amounts of money. To test our hypothesis, we employ machine learning predictive models to real data, including 407851 transactions of 4760 customers with current accounts in a local bank in Jordan. Thus, we exploit three clustering algorithms: density-based spatial clustering of applications with noise, spectral clustering, and ordering points to identify the clustering structure. The two segments of customers (generated from the clustering process) have different transactional characteristics. Our customer behavioral segmentation accuracy is, at best, 0.99 and at least 0.82. Likewise, we build three classification models using our segmented data: a neural network, a support vector machine, and a decision tree. Our predictive models have an accuracy of 0.97 at best and 0.90 at least. Our experimental results confirm that the frequency and amount of transactions of bank customers with current accounts are most likely sufficient indicators for recognizing those customers whom banks highly value. Our predictive models state that the two most critical indicators are the deposit and withdrawal transactions performed on ATMs. In contrast, the least significant indicators are the transactions of credit cards and credit cheques.