Using Machine Learning Algorithms to Predict the State of Financial Inclusion in Africa

Ismail, Qusai; Al-Sobh, Eslam; Al-Omari, Sarah S.; Yaseen, Tuqa M. Bani; Abdullah, Malak

doi:10.1109/icics52457.2021.9464590

Cited by 7 publications

(1 citation statement)

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“…e results revealed that before the financial failure, NN outperforms SVM in one-year prediction, but the opposite is true for two-year predictions. Both Gupta et al [79] and Ismail et al [80] compared SVM with other different prediction models, finding that SVM can be outperformed. Jin and Zhu [81] applied different models (i.e., NN, SVM, and decision trees) to predict the default risk of loans and showed that the SVM model and other prediction models have equal performance.…”

Section: Machine Learning Models Different Prediction Modelsmentioning

confidence: 99%

Prediction of Banks Efficiency Using Feature Selection Method: Comparison between Selected Machine Learning Models

Assous

2022

Complexity

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This study aims to examine the main determinants of efficiency of both conventional and Islamic Saudi banks and then choose the best fit model among machine learning prediction models (i.e., support vector machine (SVM), Chi-squared automatic interaction detector (Chaid), linear regression, and neural network (NN)). The data were collected from the annual financial reports of Saudi banks from 2014 to 2018. The Saudi banking sector consists of 11 banks, 4 of which are Islamic. In this study, the major financial ratios are subgrouped into the profitability ratios, managerial practices, asset and loans, capital adequacy ratios, and liquidity. First, regression analysis is implemented with efficiency ratio as a dependent variable and the proxies of banks’ profitability, liquidity, asset quality, management ratios, and capital adequacy ratios as independent variables. Next, the feature selection is applied for different prediction models. Subsequently, 4 prediction models (i.e., SVM, CHAID, linear regression, and a neural network) were developed to choose the best fit. The performance metrics have also been evaluated. Regression results exhibit that the efficiency of both conventional and Islamic banks is highly affected by profitability, liquidity, and managerial practices. Finally, we choose the best prediction model with the highest R2 in the training and the testing phases with/out feature selection that is the CHAID model. The best predictors of cost efficiency for Saudi banks are the capital ratios, namely, CAR total and CAR tier 1. Findings are theoretically and practically important to academics, investors, and policymakers. Policymakers can benefit from the novelty of this study in building an early warning system using the CHAID model to predict different financial distress scenarios.

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Section: Machine Learning Models Different Prediction Modelsmentioning

confidence: 99%