Abstract
The banking industry is a market with great competition and dynamism where organizational performance becomes paramount. Different indicators can be used to measure organizational performance and sustain competitive advantage in a global marketplace. The execution of the performance indicators is usually achieved through human resources, which stand as the core element in sustaining the organization in the highly competitive marketplace. It becomes essential to effectively manage human resources strategically and align its strategies with organizational strategies. We adopted a survey research design using a quantitative approach, distributing a structured questionnaire to 305 respondents utilizing efficient sampling techniques. The prediction of bank performance is very crucial since bad performance can result in serious problems for the bank and society, such as bankruptcy and negative influence on the country’s economy. Most researchers in the past adopted traditional statistics to build prediction models; however, due to the efficiency of machine learning algorithms, a lot of researchers now apply various machine learning algorithms to various fields, including performance prediction systems. In this study, eight different machine learning algorithms were employed to build performance models to predict the prospective performance of commercial banks in Nigeria based on human resources outcomes (employee skills, attitude, and behavior) through the Python software tool with machine learning libraries and packages. The results of the analysis clearly show that human resources outcomes are crucial in achieving organizational performance, and the models built from the eight machine learning classifier algorithms in this study predict the bank performance as superior with the accuracies of 74–81%. The feature importance was computed with the package in Scikit-learn to show comparative importance or contribution of each feature in the prediction, and employee attitude is rated far more than other features. Nigeria’s bank industry should focus more on employee attitude so that the performance can be improved to outstanding class from the current superior class.
Highlights
Today’s business environment is highly competitive and changing rapidly in terms of globalization and technology innovations, and it becomes imperative to develop the internal potential by paying adequate attention to people management and the workforce that enables the systems to operate
We evaluate the performance of the K-nearest neighbors classifier (K-NN) classifier by computing the confusion matrix shown in Figure 7. e K-NN algorithm classified the bank performance as “Superior class.” e superior class has the greatest proportion of precision, recall, F1-score, and support (Figure 7). e “support” in Figure 7 is the number of true response samples in a class
For the performance evaluation of the classifier by computing the confusion matrix, we arrived at the results shown in Figure 15. e classifier predicts the superior class of bank performance with the 81% precision
Summary
Today’s business environment is highly competitive and changing rapidly in terms of globalization and technology innovations, and it becomes imperative to develop the internal potential by paying adequate attention to people management and the workforce that enables the systems to operate. We adopted data mining technology in detecting and predicting the influence of human resources outcomes—employee skill, attitude, and behavior on bank performance. Few studies have been conducted on the application of data mining to the banking sector in the areas such as customer retention, automatic credit approval, fraud detection, marketing, and risk management [7,8,9,10, 15, 16], but none on the prediction of bank performance concerning human resources outcomes using the data mining approach. E business problem was first identified which is the growing need of the commercial banks in Nigeria to know that human resource outcomes (employee skills, attitude, and behavior) have a great impact on the performance of the bank using a data mining approach to extract all hidden facts and make a prediction. Projects a positive image of the organization to customers e supervisor helps you by doing things that are not part of his/her regular duties e supervisor keeps you informed of important events which concern you e supervisor suggests ways towards improving the work group’s performance e supervisor advises you on ways to improve your management practices
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