The Annual Fraud Indicator estimates that fraud costs the United Kingdom approximately £190 billion every year. The private sector is hit the hardest and loses around £140 billion a year, while the public sector loses more than £40 billion, and individuals lose roughly £7 billion.

The effects of fraud can be devastating on both individuals and organisations. Companies can suffer irreversible damage to reputation and be forced to close, and individuals can experience significant personal losses. Everyone should be aware of the risks and take steps to protect themselves against fraudulent activity.

Fraud detection technology

Fraud detection technology has advanced rapidly, over the years and made it easier for security professionals to detect and prevent fraud. Here are some of the key ways that Artificial Intelligence (AI) is revolutionising fraud detection - with insight from Tessema Tesfachew, the Head of Product at Avora.

An anomaly can be described as a behaviour that deviates from the expected

An anomaly can be described as a behaviour that deviates from the expected. According to Tessema Tesfachew, “Autonomous monitoring and anomaly detection specifically, have made detecting fraudulent activity faster and more accurate. Machines can monitor data 24/7 as it comes in, build patterns of behaviour that take into account seasonality and shifting trends, and identify events that don’t fit the norm.

For example, banks can use AI software to gain an overview of a customer’s spending habits online. Having this level of insight allows an anomaly detection system to determine whether a transaction is normal or not. Suspicious transactions can be flagged for further investigation and verified by the customer. If the transaction is not fraudulent, then the information can be put into the anomaly detection system to learn more about the customer’s spending behaviour online.

Accurate root cause analysis

Root cause analysis goes one step further than anomaly detection, by allowing security professionals to pinpoint what caused the anomaly. Tessema explains how an example of this would be if a system detects that the rate of fraudulent transactions has increased.

Root cause analysis would pinpoint the specific ATM or point of sale, where this increase is occurring. Swift action can then be taken to prevent fraudulent activity at that location in the future.

Fewer false positives

As mentioned, false positives can occur if a fraud detection system identifies behaviour that goes against the norm, for instance, if a customer makes a transaction in a new location. In many cases, customers are required to complete identity verification to prove that a transaction is not fraudulent.

Digital customer identity verification can help brands build a strong and reputable image. That said, forcing users to complete identify certifications regularly can cause frustration and harm the customer experience.

AI anomaly detection

AI fraud detection systems can carry out accurate data analysis in milliseconds and identify complex patterns in data

AI anomaly detection is far more accurate and results in fewer false positives. Increasing the accuracy of anomaly detection helps companies improve customer relationships and build a strong reputation. This will have a positive impact on brand image and sales revenue.

AI fraud detection systems can carry out accurate data analysis in milliseconds and identify complex patterns in data. Machines are more efficient than even the most skilled fraud analysts and make fewer errors. This is why AI fraud detection software is the preferred option in larger organisations.

Importance of fraud analysts

However, fraud analysts still play an important role in fraud prevention. Using a combination of human intervention and AI is usually the most effective approach when it comes to fraud detection. According to pymnts.com, innovative organisations now use a variety of AI and supervised and unsupervised machine learning to identify and protect against fraud.

AI systems can complete time-consuming and repetitive tasks, such as data collection and analysis. This means that fraud analysts can focus their time and attention on critical tasks that require human intervention, e.g. monitoring risk scores. AI can automate processes and enhance the quality of the fraud analysts’ work.

Conclusion

In to Tessema Tesfachew’s opinion, “Fraud detection has become vastly more efficient and effective with the introduction of Artificial Intelligence (AI). Previously, methods for detecting fraudulent activities were still data-rich, but relied more on human intervention and expert bias, and were thus, more time consuming and prone to error.

AI technology, particular anomaly detection, has streamlined fraud detection and created a more efficient, and accurate system for detecting and preventing fraud.

Covid-19 has increased the number of online transactions, which creates more opportunities for fraudulent activity. However, it also allows businesses to gain more information on their customers and enhance the capabilities of AI security software. It is more important than ever for organisations to utilise AI technology in fraud detection strategies.

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