An integrated framework to assess and improve the financial soundness of private banks

Data Envelopment Analysis on Relative Efficiency Assessment and Improvement: Evidence from Chinese Bank Branches

Early identification of ‘weak’ banks is essential for safety and soundness of the banking sector. Longer the delay in such identification, heavier the cost on an economy. In this article, we apply multiclass classification to classify banks operating in the Indian banking sector as ‘strong’ and ‘weak’ banks. Such classification is expected to provide direction to bankers for bank management. Using average return on equity (ROE) as basis for classification, we apply five machine learning models to the bank data set, namely Naïve Bayes, support vector machine, k-nearest neighbours, random forest and average neural networks. We find that all five models are able to predict the bank classes with a very high degree of accuracy. Ratio of non-performing assets to net advances turned out to be the most important variable in classifying banks as ‘weak’, followed by inflation and real exchange rate. The study is the first of its kind that successfully applies machine learning models in the Indian banking sector.

Identification of Weak Banks Using Machine Learning Techniques: Evidence from the Indian Banking Sector

In terms of image processing, encryption plays the main role in the field of image transmission. Using one algorithm of deep learning (DL), such as neural network backpropagation, increases the performance of encryption by learning the parameters and weights derived from the image itself. The use of more than one layer in the neural network improves the performance of the algorithm. Also, in the process of image encryption, randomness is an important component, especially when used by smart learning methods. Deep neural networks are related to pixels used to manipulate position and value according to the predicted new value given from a variable neural system. It also includes messy encrypted images used via applying randomness and increasing the key space in addition to using the logistic and Henon map for complexity. The main goal of any encryption method is to increase the complexity of the encrypted image to be difficult or impossible to decrypt the image without the proposed key. One of the important measurements for image encryption is the histogram and how it can be uniformed by the proposed method. Variables of randomness are used as features for the deep learning system, with feedback during iteration. An ideal image processing encryption yields high messy images by keeping the quality. Experimental results showed the backpropagation algorithm achieved better results than other algorithms.

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An Improved Image Processing Based on Deep Learning Backpropagation Technique

The use of computer-based intelligence in business applications is growing. There have been numerous arrangements previously executed, and numerous more are being discovered. The global financial crisis has heightened the significance of risk management in banks, and there has been a persistent emphasis on how risks are perceived, evaluated, and taken. For the most part, the industry has concentrated on the improvement in financial bets and current difficulties. This paper has shown that the use of artificial intelligence (AI) in the administration of banking risk, grocery store risk, check card risk, and cash risk has been found. In any case, it doesn't appear to have much to do with the ongoing business sector-level discussions centered on both executive gambling and artificial intelligence. In different regions of bank risk, executives might see a significant advantage from an investigation of how, at any point, AI can be applied to individual issues.

Machine Learning in Banking Risk Management - A Brief Overview

In the current performance evaluation works of commercial banks, most of the researches only focus on the relationship between a single characteristic and performance and lack a comprehensive analysis of characteristics. On the other hand, they mainly focus on causal inference and lack systematic quantitative conclusions from the perspective of prediction. This paper is the first to comprehensively investigate the predictability of multidimensional features on commercial bank performance using boosting regression tree. The dimensionality in the financial-related fields is relatively high. There are not only observable price data, financial fundamentals data, etc., but also many unobservable undisclosed data and undisclosed events; more sources of income cannot be explained by existing models. Aiming at the characteristics of commercial bank data, this paper proposes an adaptively reduced step size gradient boosting regression tree algorithm for bank performance evaluation. In this method, a random subsample sampling is performed before training each regression tree. The adaptive reduction step size is used to replace the reduction step size setting of the original algorithm, which overcomes the shortcomings of low accuracy and poor generalization ability of the existing regression decision tree model. Compared to the BIRCH algorithm for classification of existing data, our proposed gradient boosting regression tree algorithm with adaptively reduced step size obtains better classification results. This paper empirically uses data from rural banks in 30 provinces in China to classify the different characteristics of rural banks’ performance in order to better evaluate their performance.

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Zhe Zhang

Papers

A Machine Learning Approach to Evaluate the Performance of Rural Bank