Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

Appiahene, Peter; Missah, Yaw Marfo; Najim, Ussiph

doi:10.1155/2020/8581202

Cited by 27 publications

(18 citation statements)

References 35 publications

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“…They utilize ML methods for identifying the synthetic feature of the networks. Appiahene et al ( 2020 ) presented an integrated DEA using 3 ML methods in calculating bank performance and efficiency with 444 Ghanaian bank branches, DMUs. The outcomes are related to the respective efficacy rating attained from the DEA.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Model for Financial Distress Prediction

et al. 2022

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Predicting bankruptcies and assessing credit risk are two of the most pressing issues in finance. Therefore, financial distress prediction and credit scoring remain hot research topics in the finance sector. Earlier studies have focused on the design of statistical approaches and machine learning models to predict a company's financial distress. In this study, an adaptive whale optimization algorithm with deep learning (AWOA-DL) technique is used to create a new financial distress prediction model. The goal of the AWOA-DL approach is to determine whether a company is experiencing financial distress or not. A deep neural network (DNN) model called multilayer perceptron based predictive and AWOA-based hyperparameter tuning processes are used in the AWOA-DL method. Primarily, the DNN model receives the financial data as input and predicts financial distress. In addition, the AWOA is applied to tune the DNN model's hyperparameters, thereby raising the predictive outcome. The proposed model is applied in three stages: preprocessing, hyperparameter tuning using AWOA, and the prediction phase. A comprehensive simulation took place on four datasets, and the results pointed out the supremacy of the AWOA-DL method over other compared techniques by achieving an average accuracy of 95.8%, where the average accuracy equals 93.8%, 89.6%, 84.5%, and 78.2% for compared models.

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Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Model for Financial Distress Prediction

et al. 2022

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“…In a study by Appiahene et al [9], whether the Z-score model can effectively determine the financial crisis of listed enterprises in China was investigated, and the results showed that the Z-score model has an outstanding prediction effect for certain types of enterprises. In 2015, Fabling and Grimes [10] established a multivariate probability ratio regression model in conducting a study on financial crisis prediction.…”

Section: Related Workmentioning

confidence: 99%

Research on Early Warning of Financial Crisis of Listed Companies Based on Random Forest and Time Series

Zhang

Zhong

2022

Mobile Information Systems

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The global economy has entered a new normal, and the economic environment is evolving at a rapid pace. This requires the establishment of a financial crisis early warning system that can be dynamically analyzed based on historical data information. To address this research objective, this study proposes a k-fold random forest algorithm combined with a time series analysis model as an early warning algorithm for corporate financial crises. The algorithm takes advantage of the ability of the time series analysis model to make short-term forecasts of historical data and uses the time series analysis model to make forecasts of newly constructed financial index data. The k-fold random forest is used to analyze the financial situation of the predicted financial data and achieve the purpose of dynamic financial crisis early warning. The experimental results show that the prediction accuracy of the financial crisis early warning model based on the random forest algorithm and time series is 89%, which indicates that the model is effective and feasible.

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“…According to a citation by (Appiahene et al, 2020), an artificial neural network (ANN) is a sort of Artificial Intelligence (AI) technique that mimics the behavior of a human brain. Artificial Neural Networks (ANN) are a data processing model based on how the biological nervous system, such as the brain, processes information.…”

Section: The Artificial Neural Networkmentioning

confidence: 99%

“…Figure 2: The proposed model (Source: Author's own)3.2.1 Random Forest (RF) AlgorithmBreiman in 2001 and cited by(Appiahene et al, 2020) said the Random Forest (RF) algorithm is used for classification and prediction that employs an ensemble of classification trees. Random Forest is a learning method that entails the construction of many decision trees.…”

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confidence: 99%

Machine Learning Impact Assessment of Climate Factors on Daily Covid-19 Cases

Afrifa

Ato

Appiahene

et al. 2022

Preprint

Self Cite

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Coronavirus disease 2019 (also known as COVID-19) is a vastly infectious virus instigated by the coronavirus-2, which causes severe acute respiratory illness (SARS-Cov-2). Scientists and researchers are conducting a number of studies to better understand the COVID-19 pandemic’s behavioral nature and spread, and machine learning provides useful tools. We used machine learning techniques to study the effect of climate conditions on daily instances of COVID-19 in this study. The study has three main objectives: first, to investigate the most climatic features that could affect the spread of novel COVID-19 cases; second, to assess the influence of government strategies on COVID-19 using our dataset; third, to do a comparative analysis of two different machine learning models, and develop a model to predict accurate response to the most features on COVID-19 spread. The goal of this research is to assist health-care facilities and governments with planning and decision-making. The study compared random forest and artificial neural network models for analysis. In addition, feature importance among the independent variables (climate variables) were identified with the random forest. The study used publicly available datasets of COVID-19 cases from the World Health Organization and climate variables from National Aeronautics and Space Administration websites respectively. Our results showed that relative humidity and solar had significant impact as a feature of weather variables on COVID-19 recorded cases; and that random forest predicted accurate response to the most climatic features on COVID-19 spread. Based on this, we propose the random forest model to predict COVID-19 cases using weather variables.

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Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

Cited by 27 publications

References 35 publications

Deep Learning-Based Model for Financial Distress Prediction

Deep Learning-Based Model for Financial Distress Prediction

Research on Early Warning of Financial Crisis of Listed Companies Based on Random Forest and Time Series

Machine Learning Impact Assessment of Climate Factors on Daily Covid-19 Cases

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