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Analysing user trust in electronic banking using data mining methods

机译：使用数据挖掘方法分析电子银行中的用户信任度

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摘要

The potential fraud problems, international economic crisis and the crisis of trust in markets have affected financial institutions, which have tried to maintain customer trust in many different ways. To maintain these levels of trust they have been forced to make significant adjustments to economic structures, in efforts to recoup their investments and maintain the loyalty of their customers. To achieve these objectives, the implementation of electronic banking for customers has been considered a successful strategy. The use of electronic banking in Spain in the last decade has been fostered due to its many advantages, giving rise to real integration of channels in financial institutions. This paper reviews different methods and techniques to determine which variables could be the most important to financial institutions in order to predict the likely levels of trust among electronic banking users including socio-demographic, economic, financial and behavioural strategic variables that entities have in their databases. To do so, the most recent advances in machine learning and soft-computing have been used, including a new selection operator for multiobjective genetic algorithms. The results obtained by the algorithms were validated by an expert committee, ranking the quality of them. The new methodology proposed, obtained the best results in terms of optimisation as well as the highest punctuation given by the experts.
机译：潜在的欺诈问题，国际经济危机和市场信任危机已经影响到金融机构，它们试图以多种不同方式来维持客户的信任。为了维持这些信任度，他们被迫对经济结构进行重大调整，以努力收回投资并维持客户忠诚度。为了实现这些目标，为客户实施电子银行业务被认为是成功的策略。过去十年来，由于西班牙的许多优点，促进了电子银行业务的使用，从而实现了金融机构渠道的真正整合。本文回顾了不同的方法和技术，以确定哪些变量可能对金融机构最重要，以便预测电子银行用户之间可能的信任程度，包括实体数据库中拥有的社会人口，经济，财务和行为策略变量。为此，已经使用了机器学习和软计算的最新进展，包括用于多目标遗传算法的新选择算子。通过算法获得的结果已由一个专家委员会验证，并对它们的质量进行了排名。提出的新方法，在优化方面以及专家给出的最高标点方面获得了最佳结果。

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