Trust in Online Banking: Hard to Earn, Easy to Lose

本文探讨了在线银行如何通过限制个人信息分享、减少无关广告及提供可靠的身份验证程序来增强消费者信心。这些措施有助于提升客户信任度,进而促进在线银行业务的发展。

Respondents were also asked to suggest actions their bank could take to increase confidence in its ability to protect personal information. The most important issue with online consumers is setting limits on the sharing of personal information with third parties. This finding is also consistent with the results of our 2005 Online Consumer Permissions Study. According to that study, consumers want control over their online experience.


A second strategy to gain trust is to have fewer annoying, irrelevant ads or marketing promotions on the Web site (including ads from other online merchants). Only 21% reported that their bank's e-mails always or usually contain information they want to receive.


Finally, in response to worries about identity theft, customers want online banks to demonstrate good identity verification procedures. This might include denying access to accounts if their identity cannot be verified and notifying them as soon as possible if there has been unauthorized access to their accounts.


The rewards for listening to and respecting the concerns of online consumers can lead to greater trust. And, in the world of online banking, trust is positively related to consumer acceptance and greater use of online banking services.

 

 

It can be reached at    

http://www.computerworld.com/action/article.do?command=viewArticleBasic&articleId=101341

内容概要:本文介绍了一个基于冠豪猪优化算法(CPO)的无人机三维路径规划项目,利用Python实现了在复杂三维环境中为无人机规划安全、高效、低能耗飞行路径的完整解决方案。项目涵盖空间环境建模、无人机动力学约束、路径编码、多目标代价函数设计以及CPO算法的核心实现。通过体素网格建模、动态障碍物处理、路径平滑技术和多约束融合机制,系统能够在高维、密集障碍环境下快速搜索出满足飞行可行性、安全性与能效最优的路径,并支持在线重规划以适应动态环境变化。文中还提供了关键模块的代码示例,包括环境建模、路径评估和CPO优化流程。; 适合人群:具备一定Python编程基础和优化算法基础知识,从事无人机、智能机器人、路径规划或智能优化算法研究的相关科研人员与工程技术人员,尤其适合研究生及有一定工作经验的研发工程师。; 使用场景及目标:①应用于复杂三维环境下的无人机自主导航与避障;②研究智能优化算法(如CPO)在路径规划中的实际部署与性能优化;③实现多目标(路径最短、能耗最低、安全性最高)耦合条件下的工程化路径求解;④构建可扩展的智能无人系统决策框架。; 阅读建议:建议结合文中模型架构与代码示例进行实践运行,重点关注目标函数设计、CPO算法改进策略与约束处理机制,宜在仿真环境中测试不同场景以深入理解算法行为与系统鲁棒性。
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