Efficiency analysis and prediction in the financial services industry

金融服务业在一个国家的经济发展和增长中起着至关重要的作用。它的经济意义以及竞争激烈的市场结构促使人们进行研究，评估该行业的表现并监测其财务状况。在考虑金融服务业公司的复杂性时，数据包络分析(DEA)是一种非常合适的方法，因为它直接结合了多个输入和输出，这意味着结果将对输入和输出的复杂性和混合明显敏感。此外，DEA可以通过比较它们的实际运营结果来识别最佳做法和低效单位，而不需要任何预定义的生产函数。因此，DEA在金融服务业赢得了广泛认可。然而，有一些限制是必须考虑的。首先，数据包络分析不能处理缺失数据。其次，DEA前沿对异常值和统计噪声的存在非常敏感，由于数据错误或统计噪声，测量的效率分数可能会受到前沿上的观测数据的影响。这激励了对优化前沿的进一步研究。第三，DEA很难用于预测其他决策单元(DMU)的绩效。本研究将结合数据包络分析和神经网络两种方法来解决上述三个问题。此外，拟议的研究将把模糊逻辑整合到DEA公式中，以处理与多个定量和/或定性环境变量的跨系统比较。因此，拟议的研究提供了一种创新和独特的方法来解决DEA文献中的关键问题。它将为金融服务业的业绩分析开辟新的可能性。这也是这项研究的目标，以进一步洞察整个金融服务业的可持续增长率和潜力。

The financial services industry plays a vital role in the economic development and growth of a country. Its economic significance as well as the highly competitive market structure motivates research evaluating the industry's performance and monitoring its financial condition. Data Envelopment Analysis (DEA) is a very appropriate approach when considering the complex nature of the companies in the financial services industry since it directly incorporates multiple inputs and outputs, which means that the results will be explicitly sensitive to the complexity and mix of inputs and outputs. In addition, DEA can identify best practices and inefficient units by comparing their actual operating results without the need for any pre-defined production function.  Thus, DEA has won wide acceptance in the financial services industry. However, there are some limitations that have to be considered. Firstly, DEA can not deal with missing data. Secondly, the DEA frontier is very sensitive to the presence of the outliers and statistical noise and the measured efficiency scores can be contaminated by the observations on the frontier due to data errors or statistical noise. This motivates the further investigation of optimizing the frontier. Thirdly, DEA can hardly be used to predict the performance of other Decision Making Units (DMUs). This research will propose to integrate two methods, DEA and Neural Networks (NNs) to solve the above three issues. Furthermore, the proposed research will integrate fuzzy logic to the DEA formulation to address the cross-system comparison with multiple quantitative and/or qualitative environmental variables. As a result, the proposed research provides an innovative and unique approach to address key issues in DEA literature. It will open up new possibilities for performance analysis in the financial services industry. It is also the target of this research to gain further insight of the financial services industry as a whole in terms of its sustainable growth rate and potential.