Predictability Classes for Forecasting Clients Behavior by Transactional Data

Enhancement of Consumption Forecasting by Customers’ Behavioral Predictability Segregation

“…The first mention of dividing financial clients by their predictability can be found in ref. [6], where the authors describe the method of binary client classification based on the predictability of a certain event; clients were divided based on a dataset's median quality metric. The main idea of the method is to perform client segmentation without using a prediction model beforehand.…”

“…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.…”

“…The finance sector has long adopted machine learning techniques in their client behavior analysis (Source code can be found in ref. [1]) to evaluate credit scoring [2], predict customer churn [3], detect fraudulent transactions [4], recommend personalized entities [5], and predict next purchases or trips abroad [6]. Apart from making predictions based on customer behavior, it is wise to detect clients who would be more likely to make such predictions come true, in other words, agents with high predictability.…”