PreFIRST [Predictive Fraud Identification and Reduction Statistical Technology]

PreFIRST [预测性欺诈识别和减少统计技术]

• 批准号: 710413
• 金额: $ 12.72万
• 依托单位: MOTORING.CO.UK LIMITED
• 依托单位国家: 英国
• 项目类别: GRD Proof of Concept
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=710413
• 关键词: PreFIRST; Predictive; Fraud; Identification; Reduction

Automotive Fraud is a major source of crime in the UK and opportunities for fraud are increasing rapidly with the growth in e-marketing and online services. Based on National Fraud Authority statistics the estimate for automotive Fraud is £770 million per year. Vehicle fraud is targeted at relieving a consumer of their vehicle for no or very low cash (e.g. cheque fraud, fake buyers, deposit scams) or selling a low value vehicle for a high price (e.g. mileage scams, cloned vehicles, non-existent vehicles). As more transactions move online, it has become easier to produce digital inventory (fake websites that look genuine, fake cars with ‘genuine’ credentials). Current car checking services rely on historical data (stolen, on finance, accident etc). There is no available service that provides an alert or flag for potential fraud.The objective of this Proof of Concept is to demonstrate a step change from quantifying car data via history checks to accurately predicting attempts to defraud.The Project - “PreFIRST” - Predictive Fraud Identification and Reduction Statistical Technology - focuses on early identification of fraud. The concept is an integrated fraud management platform encompassing real time data monitoring with sophisticated behaviour detection, including social network signals from Facebook and Twitter and semantics for typical users. An innovative combination of risk monitoring and detection analytics, designedto machine learn and predict fraudulent patterns, traits and behaviours will feed into an intuitive ‘risk scoring’ tool that will alert stakeholders and provide effective decision support to combat fraud.Access to multiple live data sources will give a wider ‘big data’ view to increase the accuracy of prediction. Data from these sources is analysed and transformed into a ‘standardised’ format to allow PreFIRST to utilise data mining algorithms and knowledge discovery to evaluate and predict the likelihood of a potential fraud event

汽车欺诈是英国犯罪的主要来源，随着电子营销和在线服务的增长，欺诈机会正在迅速增加。根据国家欺诈管理局的统计数据，汽车欺诈的估计是每年7.7亿英镑。车辆欺诈的目标是以零现金或极低的现金（例如支票欺诈、假买家、存款诈骗）或以高价出售低价值车辆（例如里程诈骗、克隆车辆、不存在的车辆）来解除消费者的车辆。随着越来越多的交易转移到网上，制作数字库存变得越来越容易（假网站看起来像真的，假汽车有“真”凭证）。目前的汽车检查服务依赖于历史数据（被盗，财务，事故等）。目前还没有针对潜在欺诈提供警报或标记的服务。此概念验证的目的是展示从通过历史检查量化汽车数据到准确预测欺诈企图的一步变化。该项目-“PreFIRST”-预测欺诈识别和减少统计技术-专注于早期识别欺诈。该概念是一个集成的欺诈管理平台，包含真实的实时数据监控和复杂的行为检测，包括来自Facebook和Twitter的社交网络信号以及典型用户的语义。风险监控和检测分析的创新组合，旨在机器学习和预测欺诈模式，特征和行为，将输入到直观的“风险评分”工具中，该工具将提醒利益相关者并提供有效的决策支持，以打击欺诈。访问多个实时数据源将提供更广泛的“大数据”视图，以提高预测的准确性。来自这些来源的数据经过分析并转换为“标准化”格式，以便PreFIRST利用数据挖掘算法和知识发现来评估和预测潜在欺诈事件的可能性