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Machine Learning Systems Strengthen Fraud Detection for New Zealand Businesses

The ability to detect scams or fraud is a critical pillar for businesses across the world; more so as they migrate increasingly online. But the fact of the matter is that fraudulent transactions are extremely rare; they account for a very small percentage of activity within a company.

An online safety organisation said its new quarterly report shows that although there has been a decrease in overall scam reports, the amount of money lost has increased by 21.3% and the average loss in the past quarter was more than US$ 6,400 – an increase of 50%. The challenge is that without the proper tools and systems in place, a small percentage of activity can quickly turn into large monetary losses.

As traditional fraud schemes fail to pay off, scammers have learned to adapt their strategies. The good news is that, due to advances in fraud analytics, systems can learn, adapt, and explore different patterns for preventing fraud.

In New Zealand, scams are the leading online crime when it comes to fraud attacks, according to new research from a software company. In order to manage scams for New Zealand businesses, the software company revealed its scam solution, a fraud tracking technology that it claims has laid the foundation for digital risk protection, one of the company’s proprietary solutions. In one year, the system helped save as much as US$ 443 million for companies in the Asia Pacific region, Europe and the Middle East, by preventing potential damages with its device.

Another New Zealand software company acknowledged that businesses lose billions of dollars to online fraud every year, however, businesses respond by investing in cumbersome fraud management solutions that often rely on hand-coded rules and are difficult to keep up to date.

This company also launched a fully managed fraud detection service, which it states can detect potential fraud activity in “milliseconds.” The device has now been used by the company for over 20 years to detect potentially fraudulent online activities, such as identity and payment fraud.

Businesses can choose a pre-built machine learning model template, upload historical event data, and create decision logic to assign outcomes to predictions when using the fraud detector. For example, if the machine model predicts potential fraud activity it can trigger an investigation.

Based on the type of fraud customers want to predict, the fraud detector will pre-process the data, select an algorithm, and train a model. The 20-year historical data from the company can improve the accuracy of the trained model even if the number of fraudulent examples provided by a customer to the fraud detector is low.

In recent years, the machine learning (ML) approach to fraud detection has received considerable attention, shifting industry interest ahead from rule-based fraud detection systems and toward ML-based solutions.

That being said, there are subtle and hidden events in user behaviour that may not be evident but still indicate possible fraud. Machine learning enables the development of algorithms that can process large datasets with many variables and support the discovery of these hidden correlations between user behaviour and the likelihood of fraudulent actions.

Another advantage of machine learning systems, overrule-based systems is a faster data processing and less manual work. Smart algorithms, for instance, work well with behaviour analytics to help reduce the number of verification steps.

Today, a scam is more than just a solitary fraudulent web page; it is an entire industry with advanced technologies under the hood and highly motivated cybercriminal groups with substantial financial resources. Expertise in combating cybercrime, comprehension of threat actors’ logic, and advanced scam tracking technologies are required if businesses want not only to detect but also prevent damage caused by the scammers.


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