An adaptive sequential-filtering learning system for credit risk modeling

Credit risk and business failure classification and prediction are a major topic in financial risk management and corporate finance decision making. In this work, an adaptive sequential-filtering learning system for credit risk modeling. It is basically a three-stage sequential system for credit risk and business failure classification is presented. First, different statistical filters are applied separately to perform a preselection of relevant patterns. Second, genetic algorithms are applied to preselected patterns for refinement purpose. Finally, structural risk minimization approach based on support vector machine uses refined patterns for prediction purpose. We used three credit databases and two data partition schemes: (i) random split with 80% for learning and 20% testing, and (ii) tenfold cross-validation technique. Results from all three data sets and for all partition techniques show the effectiveness of the proposed adaptive sequential-filtering learning system for credit risk modeling against single support vector machines each with specific statistical filter-based patterns. In addition, it outperformed various models validated on the same databases. It is concluded that the presented adaptive sequential system is promising for credit risk monitoring

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state-of-the-art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten-fold cross-validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task