"Integrating Data Envelopment Analysis, Partial Least Squares and Artificial Intelligence Approaches for Risk Management in Financial Decision Domains"

点击翻译按钮获取中文摘要

Considerable research efforts have been spent on risk management in financial decision domains. Its large economic significance makes risk management research one of the most challenging research topics. The short term objective of this proposed research is to integrate Data Envelopment Analysis (DEA), Partial Least Squares (PLS) and artificial intelligence approaches to improve the current risk management models. In the long run, the objective of the proposed research is to provide an innovative and unique approach to address key issues in applying DEA, PLS and artificial intelligence approaches to risk management. It aims at opening up new possibilities for risk management in financial decision domains. It is also the target of this research to provide financial organizations with a set of practical tools to enhance their risk management capabilities in critical business areas such as credit assessment, loan portfolio optimization and security management. The significance of this research can be summarized as below: Firstly, a general analytical framework for feature selection provides an objective way to evaluate the effectiveness of feature selection. Furthermore, recursive feature elimination strategy will be introduced to PLS-based feature selection method, which can significantly enhance the prediction process and produce models with very high prediction accuracy. Secondly, the proposed research will attempt to utilize PLS based feature selection to select the most relevant inputs and outputs for DEA models. This will overcome one of the major limitations of DEA method and open up many new possibilities for DEA applications. Thirdly, imbalanced data classification is a prevalent problem in reality. The nature of the problem lies in that one class dominates the data set against other classes, which tricks the classifiers to classify everything into the dominating class in sheer pursuit of accuracy. This research proposes to integrate re-sampling and ensemble learning for information fusion to address imbalanced data classification. In the end, the industrial customer analysis platform will bring significant benefits to the financial market, including institutions and regulators.