The article is devoted to the problem of forming a set of corrective measures to deal with a set of the bank's arrears contracts. The problem at hand is topical practically for each bank. Formed set of measures should be adapted to the time and resource capacity of the employees of bank's specialized subdivision, to the predicted effectiveness of the measures and to the requirements of the bank's policy of customer loyalty. The proposed method of solving the problem of managing the contracts of arrears portfolio is based on a multi-criteria optimization model. Multicriteria is explained by the need to consider various criteria of efficiency and predicted effectiveness of the formed set of corrective measures. In order to find the optimal solution for the multi-criteria task, a special genetic algorithm is developed. The algorithm forms a set of non-dominant alternative solutions, which can be chosen by a decision maker. The article gives a brief description of the main steps of the proposed genetic algorithm and computational experiment on the basis of the developed software.
Keywords: Key words: arrears portfolio, multi-criteria optimization model, set of corrective measures, genetic algorithm, set of Pareto-optimal solutions
With the development of wearable technology, unique opportunities have emerged for providing user interaction and highly accurate personalized recognition of his work activity. The purpose of this study is to propose methods for taking into account the evaluation of the economic efficiency of human resources using big data and machine learning methods, which will allow making more informed decisions in the process of human capital management. The article proposes an approach using a hybrid neural network CNN-LSTM, aimed at determining the specific type of work performed by specialists, providing the ability to control the execution of these actions based on data from wearable devices (smart watches, smart bracelets). The accuracy of the developed algorithm in recognizing 18 different types of actions on the test sample was more than 90% according to the Accuracy metric (the proportion of correct answers).
Keywords: human capital, labor productivity, hybrid neural network, convolutional neural network, recurrent neural network