Purpose: A crucial decision in financial services is how to classify credit or loan applicants into good and bad applicants. The purpose of this paper is to propose a four-stage hybrid data mining approach to support the decision-making process. Design/methodology/approach: The approach is inspired by the bagging ensemble learning method and proposes a new voting method, namely two-level majority voting in the last stage. First some training subsets are generated. Then some different base classifiers are tuned and afterward some ensemble methods are applied to strengthen tuned classifiers. Finally, two-level majority voting schemes help the approach to achieve more accuracy. Findings: A comparison of results shows the proposed model outperforms powerful single classifiers such as multilayer perceptron (MLP), support vector machine, logistic regression (LR). In addition, it is more accurate than ensemble learning methods such as bagging-LR or rotation forest (RF)-MLP. The model outperforms single classifiers in terms of type I and II errors; it is close to some ensemble approaches such as bagging-LR and RF-MLP but fails to outperform them in terms of type I and II errors. Moreover, majority voting in the final stage provides more reliable results. Practical implications: The study concludes the approach would be beneficial for banks, credit card companies and other credit provider organisations. Originality/value: A novel four stages hybrid approach inspired by bagging ensemble method proposed. Moreover the two-level majority voting in two different schemes in the last stage provides more accuracy. An integrated evaluation criterion for classification errors provides an enhanced insight for error comparisons.
Purpose - This paper aims to explain why service-oriented business intelligence (SOBI) happened, the new development and how to make a strategy to introduce daily decision support in the retail trade. Design/methodology/ approach - The diffusion of business intelligence (BI) tools is operationalized on Rogers' innovation theory. Findings - The article answered the question: How to draft a BI strategy for all parts of the retail enterprise? By excellent data warehouse quality; choosing an area for common decision support; starting simply, with metrics (sale, gross margin, number of customers) to get users started and then continue the iterative process of practicing more comparing and personalized BI. Practical implications - Retailers meet a changeable world around where business decisions must be taken daily. In the retail industry, the customer's current demands control the supply of commodities, inventories and crew. Retailers have enterprise applications designed for their business processes, but also daily want to measure the performance. It is a question of from existing enterprise applications and databases design new decision processes and business flows that currently request BI data to be presented directly to operative responsible staff. Originality/value - Explains why there are attempts to combine the two broad architectural paradigms BI and service orientation. Service-oriented architecture, BI, on line analytical processing, extract, transform and load, SOBI are discussed in detail.