710413PreFIRST [Predictive Fraud Identification and Reduction Statistical Technology]ClosedGRD Proof of ConceptInnovate UKAutomotive 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, designed to 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