At present, the fraudsters are winning

For fraud systems tasked with keeping up with today’s online criminals, the goalposts are constantly moving. Fraud attacks constantly appear in new and unexpected spots. The number of reported fraud cases in the financial sector nearly doubled in the last year, with businesses struggling to keep up with ever-shifting vectors of attack, from new modes of mobile fraud to skyrocketing rates of identity fraud.

The fraudsters are preying on every weak point with increasingly incisive methods. They’re upping their efforts – and so should everyone else.

Traditional solutions are off the pace

The problem is this: most existing tools are not up to the task of competing with increasingly sophisticated fraudsters.

FACT: ISMG reported that only 34% of C-level leaders have high confidence in their organisation’s ability to detect and prevent fraud.

Not only that, they are unable to achieve the right balance between security and customer service that modern consumers demand (see our blog Omnichannel and risk protection: a delicate balancing act for more on this). Companies end up losing customers due to the friction caused when transactions are falsely deemed to be fraudulent because of outdated tools that can’t tell the difference.

Machine Learning alone is not the answer

In response, machine learning tools are being recruited en masse. But while they are certainly effective as detection tools, alone they don’t do enough to investigate potential fraud cases.

Data-up or human-down?

Machine learning is data-up – but to make a nuanced judgement on how best to resolve a fraud case, the human-down element is necessary too. The only way to scale this effectively is to build a tool based on human expertise, which can resolve cases based on your company’s best practice. This is where an automated decisioning tool like Rainbird comes in.

Automated decision-making can help you outsmart the fraudsters

With a company’s best fraud experts at the centre of its configuration, a Rainbird model reflects the complex understanding of an experienced fraud professional, and can apply that to thousands of cases. This human-anchored process has unique benefits that go beyond the capacities of data-up fraud solutions.

In contrast to the simple if-then approach of RPA fraud identification tools, the modelling process in Rainbird allows its decision-making to reflect the ‘greyness’ of the fraud world, resulting in more reliable outcomes.
Being based on data and logic set by business experts, rather than the huge, hard-to-fathom datasets of machine learning, means that each fraud decision Rainbird makes is transparent and accountable.

The fact that Rainbird is designed for business people to build – without the need for developers – makes it easier and quicker to react to newly identified or suspected trends by having a business’ own experts develop and maintain their solution, meaning businesses can keep pace with those ever-shifting vectors of attack discussed earlier.

So whereas traditional solutions and machine learning only detect and flag anomalies, what Rainbird can give organisations is a layer of human-like understanding on top of their RPA to transform what was previously a task automator into a decision automator.

Fraud detection is more subtle and adaptable as a result, which means there is less friction for the customer and less risk for the business. In the end, everybody wins – everybody except the fraudsters, that is.