A Secured Predictive Analytics Using Genetic Algorithm and Evolution Strategies

A. V. Krishna, Shriansh Pandey, Raghav Sarda
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Abstract

In the banking sector, the major challenge will be retaining customers. Different banks will be offering various schemes to attract new customers and retain existing customers. The details about the customers will be provided by various features like account number, credit score, balance, credit card usage, salary deposited, and so on. Thus, in this work an attempt is made to identify the churning rate of the possible customers leaving the organization by using genetic algorithm. The outcome of the work may be used by the banks to take measures to reduce churning rates of the possible customers in leaving the respective bank. Modern cyber security attacks have surely played with the effects of the users. Cryptography is one such technique to create certainty, authentication, integrity, availability, confidentiality, and identification of user data can be maintained and security and privacy of data can be provided to the user. The detailed study on identity-based encryption removes the need for certificates.
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基于遗传算法和进化策略的安全预测分析
在银行业,主要的挑战将是留住客户。不同的银行将提供各种方案来吸引新客户和保留现有客户。有关客户的详细信息将由各种功能提供,如帐号、信用评分、余额、信用卡使用情况、存入的工资等。因此,在这项工作中,试图通过使用遗传算法来确定可能的客户离开组织的搅动率。这项工作的结果可以被银行用来采取措施,以减少潜在客户离开各自银行时的流失率。现代网络安全攻击无疑对用户产生了影响。密码学就是这样一种技术,可以维护用户数据的确定性、身份验证、完整性、可用性、机密性和标识,并向用户提供数据的安全性和隐私性。对基于身份的加密的详细研究消除了对证书的需求。
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