Artificial intelligence has proven itself a powerful tool for forecasting outcomes, analyzing datasets and enhancing insight extraction. But can it tackle a massive problem like supply chain fraud? Although its applications in fraud prevention are extensive, mitigation in an extensive, interconnected logistics system is another beast entirely.
What is supply chain fraud?
The basic definition of supply chain fraud is a fraudulent activity that occurs within logistics networks. Fraud happens because these systems are too extensive and interconnected to manage — or even monitor — manually. Many fraudsters get away with their crimes, causing tremendous losses in multiple industries.
Multiple kinds of supply chain fraud exist:
- Freight fraud: This type of fraud occurs during shipping. It includes cargo theft and double brokering.
- Data manipulation: Changing inventory levels, sales figures or production rates to mislead stakeholders is a form of fraud.
- Return fraud: Returns happen often. In the retail sector, the return rate totaled 14.5% of all sales made in 2023. Sometimes, those goods are stolen or replaced in transit.
- Invoice fraud: Third parties sometimes overcharge or divert funds. Common tactics include sending false or duplicate invoices.
- Procurement fraud: This type of fraud occurs during sourcing or acquisition. The goal is to acquire contracts or obtain goods unjustly. These schemes can remain undetected for years because they are challenging to detect.
- Product substitution: Counterfeiting is a massive problem. As of 2022, the Customs and Border Protection agency has seized an estimated $2.4 billion of fakes.
Whether fraudsters swap a legitimate product for a counterfeit or manipulate invoices to overcharge their contractors, their actions cause ripple effects. Companies lose money and reputation. They may feel forced to switch suppliers, potentially causing significant disruptions. Consumers are affected, too — they may receive subpar products or no package at all.
How common is supply chain fraud?
Many types of supply chain fraud are on the rise. For instance, according to the Transportation Intermediaries Association, cargo theft increased by around 600% from November 2022 to March 2023.
Every industry has an extensive collection of suppliers, manufacturers, distributors, wholesalers and retailers. Oftentimes, shipments move internationally, further complicating the flow of goods. Bad actors — insiders and external parties — exploit this complexity to make a profit, gain unfair advantages or disrupt the system.
Attacks in the Red Sea, the Panama Canal drought and the war in Ukraine are some of the most prominent recent disruptors. When events like these interrupt the flow of goods, enterprises feel compelled to venture outside of their trusted supply networks. Those vendors are unfamiliar and unvetted, giving fraudsters an in.
Fraud is a multifaceted issue, regardless of the sector. Although it undoubtedly stems from a lack of visibility, surprisingly few can see into their extended supply network. In fact, just 13% of companies can map their entire supply chain, and 22% have no visibility beyond their primary suppliers. While software can help them close the gap, they need a more comprehensive solution.
AI’s role in fighting supply chain fraud
A single advanced machine learning model can analyze a tremendous number of data points in moments, outperforming any human, even entire teams of people. Even traditional analytical software cannot work as quickly.
Speed is not everything. Fortunately, AI outperforms conventional fraud prevention solutions in other regards. Deep learning models and neural networks can recognize context, adapt to the latest data and retain information from previous conversations. With a tool like this, organizations can respond to suspicious activity in real time.
This technology offers other benefits aside from preventing supply chain fraud. Whether firms deploy a chatbot for decision-making or a machine learning model for analysis, they will see gains. Logistics optimization can enhance operational productivity and lengthen asset lifecycles, leading to significant cost savings.
Implementation becomes increasingly expensive relative to model size. The cost of building and deploying a custom algorithm ranges from $5,000 to $150,000, depending on the project’s scope. Pricing should not be a significant barrier to adoption since small businesses will not need a huge, advanced AI to manage their supply network.
That said, while building a single advanced model may require a higher upfront investment, it simplifies maintenance and deployment because less bandwidth, memory and computation are necessary. As a result, ongoing costs may drop substantially.
How AI can mitigate supply chain fraud
There are several ways AI can reduce fraudulent activities within supply chains.
1. Fraud detection
A machine learning model can detect certain behaviors indicative of fraud that other software typically wouldn’t. This is because it can identify hidden trends and rapidly process massive amounts of information. For example, it could connect a single driver’s slightly longer idle times to a minor increase in cargo theft, helping stop even the smallest instances of fraud.
2. Predictive analytics
Predictive analytics uses machine learning to forecast outcomes. With enough historical and current data points, businesses can tell when, where and how fraudulent activity will occur. These insights can guide their risk assessments or decision-making processes.
The predictive AI market is booming because there are no comparable alternatives. It is expected to achieve a 21.9% compound annual growth rate from 2023 to 2033, reaching a value of $108 billion by the end of the forecast period.
3. Image recognition
Integrating AI into computer vision systems or surveillance cameras can help companies detect malicious insiders in warehouses, distribution centers, and on the production floor. With image recognition, they can consistently identify counterfeits or suspect workers.
4. Risk assessment
Many institutions do not even properly manage the vendors they contract with directly. According to KPMG, 43% have little to no visibility into their tier-one supplier’s performance. With AI, they don’t have to worry about this oversight. The algorithm can conduct risk assessments and periodic audits to inform them of potential fraud.
AI could help stop fraud once and for all
Although AI isn’t the end-all-be-all of technology, it is still a powerful tool capable of many things other solutions aren’t. If enough organizations use it to identify and weed out fraudsters, supply chain fraud could quickly become a thing of the past.
