Is AI changing the way we look at invoice fraud detection?

It's the end of another hectic month, and your inbox is flooded as usual. Among the emails is an invoice with a familiar logo and business name, crafted in a professional format. You instantly recognize the invoice to be from one of your trusted suppliers. Without a second thought, you process the payment for the invoice, only to later find out that you have been scammed by a fraudster. Does this sound familiar? Unfortunately, this is a sad reality for many businesses today.

According to a Forbes survey, 13% of companies reported encountering at least 20 cases of invoice fraud each year. The Association of Certified Fraud Examiners further estimates that businesses lose nearly 5% of their annual revenue to fraud. Even Amazon has fallen victim to the invoicing scam, mistakenly paying $19 million for goods it never purchased.

With fraudsters and hackers coming up with new tactics to trick businesses, invoice fraud becomes a serious issue in today's business world. However, not all is lost. With the power of generative AI, businesses can predict fraud in real-time and protect themselves from financial losses and reputational damage.

What is invoice fraud?

Invoice fraud is when fraudsters scam a business by sending fake invoices while pretending to be trusted vendors. At first glance, these fake invoices may appear almost identical to legitimate ones. However, a close inspection might reveal their sinister nature. For instance, businesses might suddenly receive invoices from look-alike email addresses with an inflated price or ask them to transfer funds to a different bank account. As fraudsters evolve, so do their tactics to fool businesses.

Here are some common types of invoice fraud that businesses face today.

Types of invoice fraud

Duplicate invoices

This is one of the most popular tactics used by scammers to swindle money from businesses. According to AudiNet, 2% of companies' invoices are duplicates. Also called double invoices, duplicate invoices are invoices submitted more than once. These invoices might take different forms. They might be identical to the original invoices or have slight alterations or get circulated through different channels, but the motive behind these invoices is all the same—to trick businesses into paying additional funds.

Phantom vendors

Phantom vendors are fictitious companies that are created by fraudsters in order to embezzle funds from businesses. These shell companies often provide services that are not tangible, making it hard for the businesses to verify. In this type of fraud, a scammer creates a fictitious vendor that never existed in the first place to solicit funds for goods or services never provided.

Internal fraud

This type of fraud occurs when an employee or anyone who has access to accounts payable software alters the details of a vendor's invoice, tricking businesses to wire the funds to a different account than that of the vendor. Sometimes, the employee might even collude with vendors and inflate the prices mentioned in the invoices in an attempt to pocket funds illegally.

Intercepted payment fraud

Most commonly known as the man-in-the-middle fraud, this type of fraud involves a fraudster impersonating the recipient and manipulating the financial transaction in an attempt to divert funds or shipments to a different account or address.

Why use AI for invoice fraud detection?

Traditionally, invoices were processed manually. As the volume of invoices increased, the entire process became more erroneous and time consuming. In addition to this, manual invoicing posed significant security risks such as theft, loss, or manipulation.

To make things easier, the entire invoice process was automated using rule-based systems (systems that work based on the set rules, like invoice processing software). Although the automated systems reduced the time spent on creating invoices drastically, they were still unable to detect new or unforeseen fraud patterns.

The advent of OCR (optical character recognition) technology has transformed the way we process data. OCR technology works by scanning printable data and converting that to machine-readable text that can be edited. While the technology facilitated the documentation process, it was unable to convert poor quality, handwritten, or complex documents. Moreover, the technology wasn't able to identify patterns, anomalies, or relationships within the extracted data. This called for technology that's flexible and intelligent to combat dynamic fraud scenarios.

How do AI fraud detectors work?

Most people have used ChatGPT at some point—whether it’s planning your dad’s birthday party or tackling work tasks. ChatGPT is a form of generative AI. At the core of generative AI is deep learning and natural language processing (NLP). Imagine teaching a toddler what the color red is. You would show relevant pictures like a red umbrella or a fire hydrant to make the child understand the concept of the color red. Deep learning works the same as you teaching a toddler. Computers are trained with big data sets to make them understand and think like humans. Just like humans use languages for speaking with others, computers use NLP to speak with us.

Today, we have several models of generative AI. One of the most popular AI architecture used in fraud detection system is GAN (Generative Adversarial Network). This type of model comprises two neural networks: a generator and a discriminator. The generator network creates synthetic data that mimics the original data while the discriminator checks the authenticity of the generated data. They both work together in a process called an adversarial process. Until the generator creates data that is very much similar to the real data, the process continues.

The benefits of employing AI in fraud detection

The benefits of using generative AI for invoice fraud detection are numerous. For instance, one of the major problems faced by traditional rule-based systems is flagging legitimate invoices as fraud (false positives), leading to unnecessary operational inefficiencies, resource consumption, time loss, and customer dissatisfaction. Generative AI has proven to be effective in reducing the occurrence of such false positives. Its ability to monitor business transactions in real time and detect potential fraud helps businesses protect themselves from financial losses.

Unlike conventional systems, which are limited by their reliance on predefined patterns and historical data, generative AI's ability to learn things dynamically has made it adept at finding new fraudulent methods. In addition to predicting patterns with existing data, generative AI can also handle large data sets and complex analytical tasks, making it helpful for businesses that handle huge volumes of transactions.

The hurdles in using generative AI for fraud detection

The accuracy of the results produced by generative AI depends heavily on the quality of datasets used for its training and analysis. However, established organizations face challenges such as maintaining complete and accurate data that is up to date. Moreover, maintaining consistent data formats and standards across all departments is tedious. To address these challenges, organizations must invest in data quality management processes.

Acquiring data sets for AI training also poses ethical and privacy challenges. As data acquired is susceptible to privacy risks, organizations must ensure they adopt ethical guidelines and privacy enhancing technologies to guarantee that data is handled with transparency, fairness, and accountability. More so, the ever-changing financial sector witnesses fraudulent activities that change with time and tide. As a result, AI-driven fraud systems require continuous monitoring and updates to maintain their effectiveness over time.

Conclusion

Although there are some internal challenges to deploy AI fraud systems, the use of AI in detecting and preventing fraudulent activities is of paramount importance to a business. Its ability to trace minute differences and find fraudulent patterns in financial transactions like invoices not only helps organizations save themselves from financial losses but also protects their company's reputation. 

By charting the right guidelines to process data and setting up a proper data quality management process, AI fraud detection systems can help businesses reduce financial risks while improving overall operational efficiency. With its advanced capabilities, AI empowers organizations to stay ahead of evolving fraud tactics, ensuring both financial security and customer trust in the long run.