PreFIRST [Predictive Fraud Identification and Reduction Statistical Technology]

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，欺诈机会迅速增加。根据国家诈骗局的统计数据，每年汽车诈骗的估计为7.7亿GB。车辆欺诈的目标是以没有现金或非常低的现金(例如支票欺诈、假买家、押金骗局)或以高价出售低价值车辆(例如里程骗局、克隆车辆、不存在的车辆)来解除消费者的车辆负担。随着更多的交易转移到网上，生成数字库存变得更容易(看起来是真的假网站，拥有“真”资质的假车)。目前的汽车检查服务依赖于历史数据(被盗、财务、事故等)。目前还没有为潜在诈骗提供警报或标志的服务。这一概念验证的目标是展示从通过历史检查量化汽车数据到准确预测诈骗企图的一步变化。项目-“PreFIRST”-预测诈骗识别和减少统计技术-专注于及早识别诈骗。这一概念是一个集成的欺诈管理平台，包括实时数据监控和复杂的行为检测，包括来自Facebook和Twitter的社交网络信号和针对典型用户的语义。风险监测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，将输入到一个直观的“风险评分”工具中，该工具将向利益相关者发出警报，并为打击欺诈提供有效的决策支持。访问多个实时数据源将提供更广泛的“大数据”视角，以提高预测的准确性。对来自这些来源的数据进行分析并将其转换为标准格式，以允许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

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