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

“整合数据包络分析、偏最小二乘法和人工智能方法进行财务决策领域的风险管理”

• 批准号: 261426-2012
• 负责人: Yang, Zijiang, Cynthia
• 金额: $ 1.38万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=547755
• 关键词: Integrating; Data; Envelopment; Analysis; Partial

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.

人们在财务决策领域的风险管理方面投入了大量的研究工作。其巨大的经济意义使得风险管理研究成为最具挑战性的研究课题之一。这项研究的短期目标是整合数据包络分析（DEA）、偏最小二乘法（PLS）和人工智能方法来改进当前的风险管理模型。从长远来看，本研究的目标是提供一种创新且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。本研究的目标也是为金融机构提供一套实用的工具，以增强其在信用评估、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：首先，特征选择的通用分析框架为评估特征选择的有效性提供了客观的方法。此外，基于PLS的特征选择方法将引入递归特征消除策略，这可以显着增强预测过程并产生具有非常高预测精度的模型。 其次，所提出的研究将尝试利用基于 PLS 的特征选择来为 DEA 模型选择最相关的输入和输出。这将克服 DEA 方法的主要局限性之一，并为 DEA 应用开辟许多新的可能性。第三，数据分类不平衡是现实中普遍存在的问题。问题的本质在于，一个类在数据集中相对于其他类占主导地位，这会欺骗分类器将所有内容分类到主导类中，以纯粹追求准确性。本研究提出将重采样和集成学习结合起来进行信息融合，以解决不平衡的数据分类问题。最终，行业客户分析平台将为金融市场，包括机构和监管机构带来显着的利益。