Addressing current account fraud using predictive analysis
Synectics Solutions have designed, piloted and deployed over 20 predictive models across 11 Tier 1 banks and insurance companies in the UK to help them improve their ability to prevent fraud, as well as substantially reducing the cost of doing so. To help companies without access to sufficiently relevant target data Synectics has also built a standardised predictive analytics fraud solution, for specific financial products, called Precision Standard. Customer Challenge Synectics Solutions recently worked with a well-known financial brand to help them deploy a successful fraud prevention solution, despite the client having no relevant product historical data in the geography they were launching in. The client wanted to ensure that when it launched its new Current Account it was optimising its fraud defences by utilising both the National SIRA fraud database, in conjunction with Synectics Precision predictive analytics capabilities. The following case study records the results of the proof of concept in preparation for the clients product launch. Business Need Optimise fraud prevention capability and reduce costs when launching new Current Account product. Solution Deployment of a standardised predictive analysis model in addition to SIRA fraud prevention solution. Benefit Ability to identify almost 70% of fraudulent applications while only investigating around 15% of referrals. Uplift in identification of fraud. Reduced false positives. Estimated savings of over £2 Million per year when product goes onto the market. Background Predictive analysis has become a widely used tool in financial services in the drive to improve fraud detection, and reduce investigation costs. However, many organisations struggle to deploy a viable predictive analytics programme because of a lack of sufficiently relevant or accurate target data, to build truly effective models. Because of Synectics unique position, as custodian of the National SIRA fraud prevention database, the company used its data science capabilities, and ability to leverage the National SIRA database, to successfully build a standardised product specific predictive fraud prevention model to identify fraud - despite the client’s lack of sufficient in-house data. Building the solution Over 2 million historical current account applications were utilised to train the target model, which included adverse fraud data from National SIRA. Over 25 data features were then used to build the model, including personal applicant details along with additional data features only available from within National SIRA, such as historical adverse flags. Once built Synectics comprehensively tested the model with the client in a proof of concept to prove its effectiveness. Results
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