fraud use case featured image

Summary

Any financial services organisation can use Rainbird’s intelligent automation platform for fraud decisioning, resulting in scalability, cost-efficiency, and, ultimately, a value-add to their business.

Challenge

Fraud in financial services is rife—in plastics, online payments, identity fraud, insurance claims and retail. The pandemic has seen opportunists further exploit operational gaps and the rates of fraud keep increasing ahead of firms’ ability to detect and mitigate. 

Most anti-fraud algorithms are machine learnt and look for patterns in data. However, the limitations of statistical methods continue to drive a very high rate of false positives, typically in excess of 95 per cent. The mitigation tends to be the recruitment of more human investigators handing more false alerts with more repayments to those who are victims.

Solution

Rainbird has rapidly developed client-specific fraud solutions, taking the proprietary knowledge of the client’s top fraud investigators and embedding that knowledge in holistic, non-linear, reusable knowledge maps which can be used to supplement existing fraud detection algorithms.

The outcome is an increase in fraud identification and a reduction of false positives by rapidly applying systematised human-like judgement on top of existing machine learnt predictions. 

This has increased fraud investigation efficiency by 100x, and because Rainbird is not subject to the bias and noise that impacts human decision-making, the quality of Rainbird’s judgements is 25% higher than the most senior experts involved in building the Rainbird models.  

Customer experience is also improved as a result of far fewer false positives.

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