Automated decision-making can save UK plc £7bn in the fight against financial fraud

  • Fraud Detection
  • 14.03.2018 12:13 pm

With the value of UK fraud rising to nearly £200 billion a year, Rainbird predicts that AI-driven automated decision-making platforms can help ‘UK plc’ make £7 billion* savings in the fight against financial fraud, over a five-year period.

The financial sector is struggling to find a balance between innovation and protection. The demands of the market are forcing financial institutions to increase the pace of new omnichannel experiences – but these experiences are at direct odds with efforts to reduce the impact of fraud. The more channels, the more “blind-spots” open to exploitation. The increasingly sophisticated attack methods being employed by fraudsters, which includes bypassing many legacy detection tools, means that the impact of fraud detection techniques is decreasing. As a result, technologies that support a more human-style reasoning are needed to detect patterns of fraud.

In addition, there has been increased investment in the advancement of fraud-detection solutions and the industry has seen an influx of advanced analytics-based software products. This is making the task of finding the right sustainable solution very challenging, with many companies purchasing limited point solutions (thinking it will save time) and then having to manually adapt those solutions to fit their company needs (as fraud is unique to every company).

Gartner’s Market Guide for Online Fraud Detection corroborates the increased financial scrutiny fraud teams are under and the challenges they face ensuring that their services are cost-effective and do not result in poor customer experience for legitimate users. Gartner strongly recommends fraud managers and architects evaluate the effect of fraud systems on the company bottom line. Furthermore, as the sophistication of digital fraud attacks increases, the need for a rethink in fraud’s functional detection and protection architecture is required, enabling organisations to strive towards more contextual, risk-based approaches that address multiple use cases.

James Loft, Chief Commercial Officer, Rainbird Technologies, said: “£7bn in predicted savings will be a sobering figure for many UK enterprises. Rainbird has already helped one credit card provider, processing over half a million transactions per minute, to automate 85% of all cases and achieve a 60% reduction in back-office fraud processing costs. Using Rainbird to avoid inconsistent human judgements our customer has increased fraud detection rates and reduced false positives, which are known to lead to around 40% of cardholders to abandon their card.”

“Financial Fraud Action UK’s Fraud The Facts 2017 report states that, as a result of investment in advanced detection and verification systems, its members prevented £6.40 in every £10 of attempted fraud. While a 64% prevention rate might be cause for some to indulge in self-congratulatory back-slapping, here at Rainbird, we don’t think that’s good enough. With our platform we want to help the industry to push that figure above the £8 barrier.”

The Rainbird platform enhances fraud detection by unifying all channels and decision sources, including behavioural and psychological. By collating data and behaviours from a large array of sources, Rainbird is able to deliver a holistic approach to detection. It makes the investigation of fraud faster, more accurate and will deliver better outcomes for customers.

The platform also has the flexibility and power to build a single customer viewpoint. Instead of teaching Rainbird about types of fraud, the focus is on the relationships between elements of a transaction. This allows for a wider variety of anomalies to be detected and for a rapid rate of response. It’s ability to adapt to different types of fraud across different channels, enables Rainbird to get ahead of fraudsters and stop a fraudulent transaction in its tracks.

Rainbird is a fully flexible platform technology (not a point solution), allowing the creation of a model that fits the unique fraud-detection requirements of any company. The build process is rapid and handled by a business’ experts (not a development team) and avoids the need to painfully customise a pre-built solution.

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