Quadient: AI has great potential to help fight against fraud

Generative AI has already revolutionised numerous business sectors by helping to unlock new possibilities and enact digital transformation worldwide. Yet, significant risks are also emerging.

There is a darker side to AI development, with generative AI tools like ChatGPT providing a new opportunity for ‘bad actors’ to conduct technical and sophisticated crimes in the digital space, making it harder for businesses to protect themselves against it.

The financial sector in particular has been hit by a wave of cyber attacks, with fintechs like Revolut having been scammed out of millions before the company was even aware of it.

Yet, there are still ways in which AI can be used for good.

AI Magazine spoke with Karim Ben-Jaafar, Senior VP at Quadient Accounts Payable by Beanworks about how AI can work to combat digital fraud cases.

How is AI helping finance departments to prevent fraud?

AI is fundamental for helping growing and increasingly automated finance functions overcome the risk of fraud, which costs UK businesses almost £200 billion (US$258bn) each year. 

As businesses scale, the number of invoices they’re expected to process rises exponentially, to a point where manual processes become impossible – both in terms of labour intensity, and the growing risk of human error. These can lead to both honest and intentional mistakes such as missing duplicate invoices or overlooking fake details.

Implementing automation helps finance teams get a grip on these issues by making processes such as data entry, moving invoices through the approval process, and tracking payments efficient, consistent and reliable. 

Embedding AI in the finance function alongside automation allows businesses to not only process more invoices, but to identify and act on the tell-tale signs of potential fraud. 

Monitoring a high volume of invoices would be impossible for human teams, but with consistent and repeatable processes, AI can instead monitor for anomalies – such as unusual data on an invoice, an invoice being sent to the wrong person, or other interruptions or deviations from process – and flag these to teams for further investigation: stopping fraud in its tracks and protecting the business’ bottom line. 

This isn’t necessarily a complex task, but it was thanks to this type of AI that one of our customers could identify more than $10,000 worth of fraudulent overpayments, which included credit and duplicate invoices.

How fundamental will AI be in future when it comes to tackling fraud?

As enterprises scale and digitise their operations, it’s vital that the finance function can keep up with digital transformation and the growing number of invoices. Both create a vast amount of data, which is impossible for finance functions to manage manually. Accuracy and human error can become serious issues, with fraudulent details or invoices overlooked. 

AI will be crucial alongside automation, helping organisations scale their operations while spotting anomalies. 

At the same time, as finance functions become increasingly digitised and sophisticated, so too will criminals. Fraudsters are already getting better at hiding their tracks from the human eye, whether creating a fake invoice or a whole fake company. And they are always innovating with new ways to commit fraud. 

Sufficiently sophisticated AI-powered algorithms will be able to spot these fakes from a mile off, by automatically scanning for any anomalies and flagging suspicious activity to the right person. For example, AI might not identify a specific scam, but it can spot suspicious changes to payment details, or if a company has suddenly changed its address on an invoice. 

Importantly, these flags will happen in real-time, so the finance team can act quickly before payments are made.

What are the main challenges to overcome when it comes to implementing AI technology in fraud prevention?

Many organisations are missing out on the benefits of embedding AI into the finance function because of organisational conservatism and the initial costs of implementation. It is true that AI and automation can be expensive and complex to adopt, but the benefits far outweigh the costs or any potential inconvenience. 

To combat this challenge, the finance function must get organisational buy-in by demonstrating the wider business need for AI. For example, in the current economic climate, it is more important than ever not to become a victim of fraud and lose profit. 

An AI-powered finance function can make the difference in being able to quickly spot any fraudulent anomalies and increase accuracy so that the organisation can remove that risk altogether. 

Similarly, an AI that can spot anomalies in automated processes could be used almost anywhere in the business to identify potential issues before they become a problem.

Another argument teams can use is the reputational cost of becoming a victim to or enabler of fraud. Suppliers and customers will be worried about the potential impact on their own business, whether becoming victims themselves or not receiving payments for months. 

Even if their own organisation has not been impacted, they are much less likely to trust a partner that has shown it cannot identify or prevent fraud. It's therefore vital the finance function works with other internal stakeholders to highlight the benefits from AI-powered accounting tools.

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