2 research outputs found
Modeling the Telemarketing Process using Genetic Algorithms and Extreme Boosting: Feature Selection and Cost-Sensitive Analytical Approach
Currently, almost all direct marketing activities take place virtually rather
than in person, weakening interpersonal skills at an alarming pace.
Furthermore, businesses have been striving to sense and foster the tendency of
their clients to accept a marketing offer. The digital transformation and the
increased virtual presence forced firms to seek novel marketing research
approaches. This research aims at leveraging the power of telemarketing data in
modeling the willingness of clients to make a term deposit and finding the most
significant characteristics of the clients. Real-world data from a Portuguese
bank and national socio-economic metrics are used to model the telemarketing
decision-making process. This research makes two key contributions. First,
propose a novel genetic algorithm-based classifier to select the best
discriminating features and tune classifier parameters simultaneously. Second,
build an explainable prediction model. The best-generated classification models
were intensively validated using 50 times repeated 10-fold stratified
cross-validation and the selected features have been analyzed. The models
significantly outperform the related works in terms of class of interest
accuracy, they attained an average of 89.07\% and 0.059 in terms of geometric
mean and type I error respectively. The model is expected to maximize the
potential profit margin at the least possible cost and provide more insights to
support marketing decision-making
Operational research and artificial intelligence methods in banking
Supplementary materials are available online at https://www.sciencedirect.com/science/article/pii/S037722172200337X?via%3Dihub#sec0031 .Copyright © 2022 The Authors. Banking is a popular topic for empirical and methodological research that applies operational research (OR) and artificial intelligence (AI) methods. This article provides a comprehensive and structured bibliographic survey of OR- and AI-based research devoted to the banking industry over the last decade. The article reviews the main topics of this research, including bank efficiency, risk assessment, bank performance, mergers and acquisitions, banking regulation, customer-related studies, and fintech in the banking industry. The survey results provide comprehensive insights into the contributions of OR and AI methods to banking. Finally, we propose several research directions for future studies that include emerging topics and methods based on the survey results