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Using AI to Eliminate Digital Financial Fraud – Will It Work?

Artificial intelligence (Al) makes it possible for machines to perform human-like tasks. It has become more popular today due to its increase in advanced algorithms, data volumes and computer power and storage improvements. Indeed, 72% of business decision-makers believe that Al is crucial for the future of business.

But what does it do for our money? Before we can answer this, it’s important to consider the scale of the extent of the link between our finances and technology.

People like their money and technology fast and easy, which is why fintech innovations have made it easier for consumers to connect with financial services. We can shop, save, gamble, bank and pay the bills on the go with a couple of swipes and clicks of a smartphone. But, as so often in life, travelling at speed does carry a risk and when it comes to our finances and the risk comes in the shape of fraud.

Fraud has been an issue for financial services for a very long time – including pre-digital days – but the good news is that artificial intelligence has the potential to make a huge reduction in financial fraud. With automated fraud detection tools getting smarter, machine learning has gained more power and the outlook is looking very positive. The biggest threat to fraud is detection – and if machines can learn to detect things that don’t quite look right then we’ll be in a much stronger position going forward.

With millions of transactions happening every day, Al is helping to evaluate behavioural patterns and data points to approve transactions in real time. It’s also worth noting that artificial intelligence is nothing new when it comes to fraud prevention as businesses such as PayPal have been using it for years, it has just become more widespread and is only to increase in the future.

With machine learning, computer systems learn, predict and act in a more intuitive way, consuming data and building up knowledge. This comes in two forms:

Supervised learning: consists of a model that is given historical data and has been classified as either fraudulent or non-fraudulent. This data is used to train the machine learning algorithm and will then be able to recognise fraudulent activity.

Unsupervised learning: this processes large amounts of data to recognise abnormal behaviour and finding new ways to prevent attacks and identifying accounts associated with the attack.

Neither is a ‘silver bullet’ in their own right. Supervised learning requires some human input and will only find fraud that is similar to previous attacks. Unsupervised machine learning, meanwhile, suffers from fewer domain problems and therefore isn’t always as effective when it comes to stopping low volume attacks.

However, by using the two different models it can help your business combat fraud patterns and notice new methods – and when used together this can work very well. If AI is to work to combat fraud, this mix is key. The right level of human input, with the right level of technological complexity, can leave a powerful tool to spot and stop fraudulent activity.

Overall, Artificial Intelligence helps to make real-time decisions in a fast-paced environment, learn more, make accurate decisions and process data faster, all of which could and should improve accuracy, especially in an era in which the threat becomes more complex.

More and more companies are turning to technology for help with financial fraud and, even though challenges still remain, Al and machine learning make lives easier as the fintech sector continues to expand. 

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