A divide-and-conquer strategy using feature relevance and expert knowledge for enhancing a data mining approach to bank telemarketing

The discovery of knowledge through data mining provides a valuable asset for addressing decision making problems. Although a list of features may characterize a problem, it is often the case that a subset of those features may influence more a certain group of events constituting a sub-problem within the original problem. We propose a divide-and-conquer strategy for data mining using both the data-based sensitivity analysis for extracting feature relevance and expert evaluation for splitting the problem of characterizing telemarketing contacts to sell bank deposits. As a result, the call direction (inbound/outbound) was considered the most suitable candidate feature. The inbound telemarketing sub-problem re-evaluation led to a large increase in targeting performance, confirming the benefits of such approach and considering the importance of telemarketing for business, in particular in bank marketing

Influence of artificial intelligence on public employment and its impact on politics: A systematic literature review

Goal:Public administration is constantly changing in response to new challenges, including the implementation of new technologies such as robotics and artificial intelligence (AI). This new dynamic has caught the attention of political leaders who are finding ways to restrain or regulate AI in public services, but also of scholars who are raising legitimate concerns about its impacts on public employment. In light of the above, the aim of this research is to analyze the influence of AI on public employment and the ways politics are reacting. Design / Methodology / Approach: We have performed a systematic literature review to disclose the state-of-the-art and to find new avenues for future research. Results: The results indicate that public services require four kinds of intelligence – mechanical, analytical, intuitive, and empathetic – albeit, with much less expression than in private services. Limitations of the investigation: This systematic review provides a snapshot of the influence of AI on public employment. Thus, our research does not cover the whole body of knowledge, but it presents a holistic understanding of the phenomenon. Practical implications: As private companies are typically more advanced in the implementation of AI technologies, the for-profit sector may provide significant contributions in the way states can leverage public services through the deployment of AI technologies. Originality / Value: This article highlights the need for states to create the necessary conditions to legislate and regulate key technological advances, which, in our opinion, has been done, but at a very slow pace.info:eu-repo/semantics/publishedVersio

A decision support system for fund raising management based on the Choquet integral methodology

The employment of a decision support system for optimizing fund raising strategies is crucial to the management of non-profit organizations. Commonly considered methodologies utilize the organization’s donor database in order to gather and analyse information. However, many organizations, especially small- and medium-sized ones, do not own or efficiently manage a database, and consequently, the usual methods are inapplicable. In this paper, a decision support system is developed that is able to identify the most promising fund raising strategies on the basis of the organization’s profile. The profile factors of a non-profit organization are analysed and hierarchically organized in a decision tree in order to effectively employ the Choquet integral methodology, which is recommended in these kinds of multi-criteria decision problems. The results obtained in the real operational context show the effectiveness of the proposed system

A decision support system for fund raising management based on the Choquet integral methodology

The employment of a decision support system for optimizing fund raising strategies is crucial to the management of non-profit organizations. Commonly considered methodologies utilize the organization\u2019s donor database in order to gather and analyse information. However, many organizations, especially small- and medium-sized ones, do not own or efficiently manage a database, and consequently, the usual methods are inapplicable. In this paper, a decision support system is developed that is able to identify the most promising fund raising strategies on the basis of the organization\u2019s profile. The profile factors of a non-profit organization are analysed and hierarchically organized in a decision tree in order to effectively employ the Choquet integral methodology, which is recommended in these kinds of multi-criteria decision problems. The results obtained in the real operational context show the effectiveness of the proposed system