Fuzzy Refinement Domain Adaptation for Long Term Prediction in Banking Ecosystem

Long-term bank failure prediction is a challenging real world problem in banking ecosystem and machine learning methods have been recently applied to improve the prediction accuracy. However, traditional machine learning methods assume that the training data and the test data are drawn from the same distribution, which is hard to be met in real world banking applications. This paper proposes a novel algorithm known as fuzzy refinement domain adaptation to solve this problem based on the ecosystem-oriented architecture. The algorithm utilizes the fuzzy system and similarity/dissimilarity concepts to modify the target instances' labels which were initially predicted by a shift-unaware prediction model. It employs a classifier to modify the label values of target instances based on their similarity/dissimilarity to the candidate positive and negative instances in mixture domains. Thirty six experiments are performed using three different shift-unaware prediction models. In these experiments bank failure financial data is used to evaluate the algorithm. The results demonstrate that the proposed algorithm significantly improves predictive accuracy and outperforms other refinement algorithms.