An Efficient Machine Learning Model for Location Aware Credit Fraud and Risk Classification and Detection

V. Muthulakshmi, C. Saravanakumar, A. Tamizhselvi
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Abstract

. Online transaction grows in enormous rate because of the strength of usage by the user. User always use online mode to pay the amount to the respective merchant. Various method of payment is available in the market but credit card is so popular due to the pre credit is assigned to the customer by banker. Card user gets extra time for paying the payment which gives comfortable live to them. Security of the card suffers in various factors such as theft, fraud, illegal access, so it is protected by using modern algorithm with automated capability. Artificial Intelligent algorithms are applied to detect the fraud but that is not achieving enough accuracy. This type of problem is overcome by using location based risk identification model with multidimensional features for analysis. Three phases of processing is carried out namely feature management, risk management and Location awareness. The focus of the model is to protect the credit card frauds in multi level security by identifying the source and location of access. It achieves high level of security when compared to all exiting algorithms with reliable manner.
位置感知信用欺诈的高效机器学习模型及风险分类与检测
. 由于用户的使用强度,网上交易以惊人的速度增长。用户总是使用在线模式支付金额给相应的商家。市场上有各种各样的付款方式,但信用卡之所以如此受欢迎,是因为银行给客户分配了预信用额度。信用卡用户可以获得额外的付款时间,让他们过上舒适的生活。信用卡的安全性受到盗窃、欺诈、非法访问等各种因素的影响,因此采用具有自动化功能的现代算法进行保护。人工智能算法被应用于检测欺诈,但没有达到足够的准确性。利用具有多维特征的基于位置的风险识别模型进行分析,克服了这类问题。处理过程分为三个阶段,即特征管理、风险管理和位置感知。该模型的重点是通过识别访问源和访问位置,在多层次上保护信用卡诈骗。与现有的所有算法相比,它以可靠的方式实现了较高的安全性。
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