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

在财务决策领域的风险管理方面已经花费了相当多的研究努力。其巨大的经济意义使风险管理研究成为最具挑战性的研究课题之一。本研究的短期目标是整合数据包络分析（DEA），偏最小二乘法（PLS）和人工智能方法，以改善目前的风险管理模型。从长远来看，拟议研究的目标是提供一种创新和独特的方法来解决应用DEA，PLS和人工智能方法进行风险管理的关键问题。它旨在为财务决策领域的风险管理开辟新的可能性。本研究的另一个目标是为金融机构提供一套实用的工具，以提高其在信用评估、贷款组合优化和安全管理等关键业务领域的风险管理能力。本研究的意义可以概括为以下几点：第一，特征选择的一般分析框架提供了一个客观的方法来评估特征选择的有效性。此外，递归特征消除策略将被引入到基于PLS的特征选择方法中，这可以显著地增强预测过程，并且产生具有非常高的预测精度的模型。其次，本研究将尝试利用偏最小二乘法为DEA模型选择最相关的输入和输出。这将克服DEA方法的一个主要局限性，并为DEA的应用开辟了许多新的可能性。第三，不平衡数据分类是现实中普遍存在的问题。问题的本质在于，一个类在数据集上占主导地位，而其他类则占主导地位，这会诱使分类器将所有内容都分类到占主导地位的类中，以追求准确性。本研究提出整合重采样与集成学习的资讯融合方法，以解决资料分类不平衡的问题。最终，行业客户分析平台将为包括机构和监管机构在内的金融市场带来重大利好。

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.