Deep learning and forecasting in practice: an alternative costs case

The usage of machine learning methods in the financial sector, regarding repayment prediction or forecasting, is quite a new topic, constantly gaining in importance. The concept of the alternative costs in the literature covering machine learning and deep learning occurs most often in connection with the non-financial areas as costs of lost benefits. This empirical paper presents research dedicated to deep learning used in forecasting the alternative costs of leasing represented by the variable KUK_PRC. The study is based on the experimental approach and uses real organization data to solve the forecasting problems in the financial area with AI solutions. This research contributes to the science by identifying and exploration of the research gap in the field of applied economics and finances. The main finding of this paper is the proposed forecasting ACSeq-DNN model that forecasts opportunity costs with smaller deviations from actual values than the forecasting achieved by state-of-the-art models.