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Aligning fraud prevention to revenue growth

Machine Learning Powered Fraud Prevention Fraud inhibits growth. And while digital commerce has flourished due to pandemic related restrictions so too have fraudsters, taking advantage of the increase in online activity to expand the scale and variety of their attacks. Given the increasing sophistication of these attacks, it is challenging for businesses to stay ahead of the latest threats. A recent study by Juniper Research indicates a 16% growth in ecommerce fraud losses, with $41 billion lost worldwide in 2022. In a constantly evolving fraud environment, fraud prevention systems that rely on a traditional rules-based approach will struggle to adapt, with rules quickly becoming outdated, causing real customers to be blocked whilst failing to identify the latest fraud patterns. So how can firms still deliver enhanced fraud prevention without impacting customer experience and conversion? How can they improve fraud detection accuracy when attacks are more sophisticated? The most powerful solution to meet this challenge is the integration of Machine Learning (ML) into fraud strategies. The complex ML models enable businesses to increase fraud detection whilst more accurately identifying genuine customers.

Sophisticated threats lead to increased losses There has been considerable growth in global cyber fraud during the last year. The catalyst for this upswing is the seemingly easy access fraudsters have to stolen credit card, bank account, and payment information via the dark web. When combined with the large volume of identity information available through data breaches, the severity of this problem is significant. The extent of this problem is compounded by the growing level of organisation within fraud networks and their use of technology to create increasingly elaborate methods of conducting fraud. It is worth remembering that whenever a breakthrough in technology becomes available to organisations, it also becomes available to fraudsters.

It is therefore apparent that fraud prevention which relies on hard-coded rules-based systems often lacks precision when dealing with sophisticated fraud. This means that fraudsters slip through while many legitimate customers are rejected through false positives – leading to a considerable loss in potential earnings.

Fraud is a problem but so are false rejections There is no doubt that fraud is a heavy cost for businesses. However, the effort to prevent fraudulent transactions has arguably fuelled an even bigger problem – legitimate customers that are erroneously rejected. In fact, research suggests that the cost of false rejections can often amount to significantly more than the value of fraud losses. This highlights the scale of the problem – whereby firms are not only facing losses due to increased fraud but also considerable loss of revenue due to false positives from inaccurate fraud prevention systems. One of the biggest pain points for identity and fraud decision-makers is the battle between fraud prevention and revenue generation. Customer expectations of low friction vs. increased fraud threats For high-growth businesses, strong conversion is incredibly important to meet revenue targets. Conversion is essentially when a user completes the desired action online, such as making a purchase or completing an application form. Fraud prevention is therefore a delicate customer experience (CX) balance, with some businesses in the ecommerce sector willing to accept higher fraud rates to keep conversion rates high. But, as the tactics used by fraudsters become more sophisticated, this approach must change – otherwise a greater proportion of revenue will be lost to fraud. Advantages of Machine Learning based fraud prevention systems ML models are the most efficient way for companies to predict which of their transactions are fraudulent or are likely to result in chargebacks. This is due to the vast amount of data that can be analysed with Machine Learning. This efficiency can significantly reduce costs while providing higher levels of accuracy than other approaches to fraud detection. As ML is more effective than humans at identifying fraud patterns and more adaptive than fixed rule sets, it means this approach can drastically lower the false positive rate and allow more genuine customers to complete their purchases/applications. In addition to this, another major benefit of ML is that the models become more accurate over time by consuming large amounts of additional data. Many online companies generate huge volumes of data that can consistently be used to hone the accuracy of their ML model. The more data that is used to train the model, the more accurate the result will be. Machine Learning vs. rules-based fraud prevention systems In the past, many companies relied on a purely rules-based system to prevent fraud.

As fraud techniques have become more sophisticated the inadequacies of this type of system have become more apparent. Rules-based systems quickly become convoluted and contradictory, increasing the false positive rate and number of manual reviews. They are also limited by human understanding as they are created manually and can miss subtle correlations in the data, especially in newly emerging fraud patterns. Another problem associated with rules-based fraud prevention systems occurs during peak events, such as ‘Black Friday’. At times like these when transaction volumes increase dramatically there is a corresponding rise in manual reviews, as rule thresholds are exceeded. Consequently, businesses that solely rely on rule-based fraud prevention need to have additional fraud agents available to avoid large backlogs of review cases. In contrast, Machine Learning fraud detection can consistently maintain the same high levels of accuracy with no added manual effort required during sales peaks. ML models consist of a set of algorithms that combine all its known features to assess each transaction. The value of an ML model is that it is trained to continuously query the features and compares the results in order to make the best possible fraud assessment. This happens in a matter of milliseconds. The power of ML is that it can identify patterns and make inferences from previously unconnected data. The future of Machine Learning fraud prevention There is no doubt that ML is going to remain an integral part of fraud prevention in the future. As more sectors mature in their fraud prevention programs there is a possibility that sharing data between organisations and regions – while maintaining privacy – becomes widespread. This global sharing of data is known as collective intelligence and could take our current application of ML fraud prevention to the next level. Incorporating such vast quantities of data would allow for even greater accuracy in fraud detection. In theory, this could eventually eliminate fraud as all the possible ways to commit fraud were identified and added to the global fraud prevention model. Although this may seem unrealistic the potential for collective intelligence is clear. Consumer sentiment towards data sharing is also improving as the security benefits associated with sharing data with trusted businesses become more widely understood. According to our global research of over 6,000 consumers worldwide, 56% INDICATED that they would be willing to allow different companies to share their personal data with each other to ensure greater online security and proactively avoid being the victim of fraud.

As the level of sophistication and the organisation of online fraud syndicates becomes more advanced, the best defence lies in technology like ML fraud identification and collaboration. Collective intelligence sharing between businesses could help them stay ahead of fraudsters and reduce the impact of fraud. For more information see Experians full guide on:

Written by Experian Danemark

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