Artificial intelligence (AI) is increasingly pervasive in today’s business landscape, transforming various aspects of daily interactions. This influence extends to accounts payable and receivable processes where advanced algorithms and machine learning streamline digital transactions.
However, as more organizations increasingly rely on automated systems, the sophistication of fraudulent invoicing schemes has also evolved, incurring significant financial losses. Ultimately, just as AI has been fundamental to executing advanced payment scams, it is also essential to combating them.
What’s invoice fraud?
Invoice fraud refers to various deceptive practices aimed at obtaining money through false or manipulated accounts payable statements.
For example, criminals generate fictitious invoices for products or services never provided, hoping the targeted business will pay without verifying. In some instances, they modify legitimate invoices to inflate amounts owed or change payment details, tricking organizations into paying more than they should or sending money to the wrong account.
According to research, invoice fraud has increased by over 75 percent since 2020, costing U.S. companies approximately $300,000 yearly. Unfortunately, businesses often struggle to detect the problem until it’s too late. They may not find out they’ve fallen victim until the vendor reaches out, wondering why they haven’t received payment yet.
The role of AI in perpetrating invoice fraud
Criminals increasingly leverage artificial intelligence to execute more sophisticated invoice fraud schemes, marking a significant evolution in cybercrime tactics. These activities span multiple categories, each more difficult for traditional detection methods to identify.
Business email compromise (BEC) campaigns
Hackers infiltrate a company’s email system and manipulate communications to issue fraudulent invoices, often posing as trusted executives. What’s even more worrying is the ease of access to these systems.
In December 2023, a cybercriminal group known as GXC Team announced the release of a new AI-powered business invoice swapper tool to execute BEC attacks. This platform can automatically swap bank account details on invoices, evade font detection measures, and process fund transfers.
The group made the tool available for rent at $2,000 weekly or a one-time purchase fee of $15,000. This means fraudsters don’t even need to be tech-savvy anymore to pull off expensive scams.
Data mining for target identification
AI can analyze publicly available company profile data to identify potential targets based on their payment behaviors and vulnerabilities. For example, threat actors use AI web-scraping tools to collate information from business directories and social media, identifying companies with less robust financial controls. They then target these businesses with tailored invoice fraud schemes.
Machine learning (ML) for pattern recognition
Criminals can apply ML algorithms to study payment patterns and predict when businesses will likely process invoices. This allows them to time their attacks for maximum effectiveness.
Furthermore, AI-driven natural language processing techniques allow fraudsters to craft communications mimicking the tone and style of a company’s finance department. These NLP algorithms make the fraudulent requests appear legitimate and trustworthy, extending the detection time frame.
Deepfake technology
Sophisticated cybercriminals leverage advanced deepfake technology to create realistic audio or video impersonations of executives to execute invoice fraud.
A recent high-profile case involved a hacker impersonating the company’s chief financial officer in a video call with the finance team. During the call, they requested an urgent payment of $25.6 million, which the unsuspecting employees promptly paid out.
How automation combats AI-enhanced invoice fraud
Several companies are turning to advanced automation strategies to fight fire with fire and identify scams much quicker. In fact, fraud detection is the number one use case for AI in the financial industry. This approach comprises multiple measures, which combine to ensure more robust prevention processes.
Automated invoice verification
AI algorithms can cross-check invoices against critical details like purchase orders, contracts, and delivery receipts to ensure consistency and legitimacy. For example, advanced automated processing platforms employ three-way matching verifications to confirm accounts payable transactions. These tools integrate with existing enterprise resource planning systems to match invoices with purchase orders automatically.
ML-enhanced anomaly detection
Just as ML can identify patterns for fraudulent purposes, it can also be trained to detect anomalies based on historical transaction data. The system analyzes and compares vast amounts of information to pinpoint unusual activities indicative of criminal actions. For example, consultancy firm Ernst & Young’s proprietary ML tool can prevent invoice fraud in logistics with up to 97 percent accuracy.
AI-enabled workflow approval processes
Automated workflows ensure all invoices go through predefined approval processes before releasing payments. For example, companies can institute a system of multi-level approvals for invoices over a certain threshold. This approach eliminates the human vulnerability factor, reducing the risk of unauthorized fund disbursements.
Using AI to tackle broader cybersecurity challenges
Invoice fraud represents a minuscule aspect of the ever-growing cybercrime framework. The longer these underlying issues go unanswered, the more losses businesses incur. Reports show data breaches cost an average of $2.4 million, underscoring the need for a resilient fraud risk management policy.
As threats continue to evolve in complexity and scale, AI tools will be critical to enhancing cybersecurity defenses. A key aspect of this is automated threat detection and response. AI algorithms can process vast amounts of data in real-time to identify potential attacks and immediately neutralize them.
Another standout feature of AI cybersecurity is predictive analytics. With the right training, AI can anticipate potential threats before they occur, enabling businesses to be proactive in preventing and managing risks. For example, security platform CrowdStrike utilizes predictive AI to forecast where attacks may occur based on emerging threat patterns, allowing organizations to strengthen defenses in advance.
Deploy artificial intelligence to address invoice fraud
As e-invoicing becomes the norm, AI’s potential to perpetrate and prevent criminal activities will only grow. With criminals leveraging advanced tools to enact sophisticated attacks, businesses cannot afford to slack on AI cybersecurity, both in combating invoice fraud and minimizing overall exposure.
Zac Amos is the Features Editor at ReHack, where he covers business tech, HR, and cybersecurity. He is also a regular contributor at AllBusiness, TalentCulture, and VentureBeat. For more of his work, follow him on Twitter or LinkedIn.
TNGlobal INSIDER publishes contributions relevant to entrepreneurship and innovation. You may submit your own original or published contributions subject to editorial discretion.