Dynamic Classification of Bank Clients by the Predictability of Their Transactional Behavior

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

“…The monograph of [27] used the k -means algorithm to segment customers' behavior in electronic and traditional banking in Iran. The paper of [11] demonstrated a study on classifying bank customers based on their transactions in Russia using the well-known long short-term memory network method. The monograph of [19] uses two well-known segmentation techniques, k-means, and fuzzy c-means, to divide customers based on their transactions in Turkey.…”

Enhancing Predictability Assessment: An Overview and Analysis of Predictability Measures for Time Series and Network Links

“…In [87,88], the authors assess the realized predictability (RPr) of client's transactional sequences by employing a coefficient based on the mean absolute error of the selected predictive model for each sequence. Subsequently, they categorize all sequences into predictability classes based on the values of the predictability measure.…”

Enhancement of Consumption Forecasting by Customers’ Behavioral Predictability Segregation

“…However, a client's predictability is bound to change, which was taken into account in ref. [7]. Not only did the authors use incremental learning techniques for dynamic classification, but they also described the procedure for classifying actors into 32 classes according to the predictability of five chosen transactions.…”

“…In this paper, we use the same general idea as in refs. [6,7], including the application of an LSTM model (a recurrent neural network with long short-term memory [9]), but with several alterations: actors are classified based on a forecast quality threshold (similar to [8]) of all transactions; predictions are calculated on all levels, not just the micro-one; the classes of actors are utilized to lower the forecast uncertainty of all clients.…”