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Robocop: How machine learning has its eyes set on internal expense fraud

If expense fraud is the problem – is machine learning the remedy?

By James Nunns

Since 2014, Artificial Intelligence algorithms have been learning, and beating humans at their favourite games – from the video console Atari to the classic board game Go. But, fast-forward to today, and their capabilities go far beyond just conquering human hobbies.

In fact, Deep Mind has recently managed to crack the problem of memory and has

Chris Baker, Managing Director: UK, Nordics & MEA, Concur.

gifted its neural networks the ability to not only learn but retain information and use it again. And it’s not just memory. Researchers have also created an AI that has developed its own language. A significant leap forwards from simply beating Atari high-scores.

Machine learning has developed at break neck speed in the last decade and a recent Forrester Wave report forecasts 15 percent compound annual growth for the market through 2021. Computers can now read, learn and speak, and do so more intelligently every day.

For many, the rise of super-intelligent robots points to one conclusion: impending Armageddon. People are terrified that bots will snatch their jobs and eventually take over the world. Two-thirds of Americans believe robots will soon perform most jobs, rendering humans useless.

However, robot-induced oblivion isn’t expected any time soon. And when it comes to machine learning, businesses shouldn’t shy away. Instead, they should embrace it and make it work for them. The real-world business applications of machine learning are infinite. Take Sift Science, which plans to use machine learning and artificial intelligence to predict and prevent fraud everywhere online. It’s a potentially huge development, especially when you consider the fact that card issuers and banks could be saving $12 billion annually with the help of intelligent fraud detection systems.

We know machine learning is already proving its worth in the financial services sector, so there’s no reason why it can’t lend a hand to enterprise technology. And more specifically, the process that time forgot – the tedious expenses process.

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Fraudulent expenses have long plagued the workplace, and no technology has yet risen up and squashed them for good. With machine learning though, we are definitely moving in the right direction. But how much of an issue is internal expense fraud? And what might an expense-fraud busting bot look like?

When it comes to expenses, the mantra “rules are made to be broken” is followed with much zeal. In fact, dodgy expenses are increasingly becoming a cultural norm, and it’s having a significant impact on bottom lines. But it’s not just senior executives pushing through their Sunday lunch. This year, the Conservative party was fined £70,000 for failing to report its expenses throughout 2014 and 2015 – and over twelve police forces have filed cases so far against the party for the fraud. Our recent research found that 23 per cent of employees thought it was acceptable to fiddle their expenses, coupled with the Financial Fraud Action (FFA) revealing a financial scam was committed once every 15 seconds in the first half of 2016.

If expense fraud is the problem – is machine learning the remedy?

Perhaps. AI’s ability to analyse huge swathes of data and spot patterns could be just the help the issue of workplace fraud needs. Within multi-national companies, finance teams have to file through hundreds, if not thousands of tedious claims. Its tough work and human error can’t be avoided. However, bots don’t get bored, so the likelihood of one missing a dodgy-looking expense is far more unlikely.

Of course, this doesn’t mean that the finance team will be replaced with an army of robots. Instead, it will simply make their lives that bit easier by taking on the heavy lifting.  And technology can only work effectively as long as there’s someone monitoring and using the data in clever ways – showing the need for both bot and body.

However, while bots won’t make human errors, you cannot assume that everything they do is correct. This is exactly what Stanislav Petrov did in 1983 when he questioned the fool-proof computer which detected an ‘incoming missile’ from the United States – the protocol was to retaliate with a nuclear attack – but Petrov suggested the computer was wrong, by using his brain, and saved the world in the process.

Machine learning offers a world of possibilities. It can do manual jobs in a timely manner – freeing up workers to focus on more business-critical jobs. And importantly, it can act as a deterrent to workplace expense fraud. If workers know that a bot is screening all claims – they might just think twice before submitting that receipt from Friday night drinks.

We live in a digital world, and AI is already present in society, from the bedroom to the boardroom. It’s only a matter of time before it is assisting all industries and sectors. With this in mind, it’s in a finance department’s best interest to supercharge their outdated Excel docs into something more apt for the modern world. It’s an inevitable progression, and if businesses embrace it now, rather than later, they could save themselves a small fortune.

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