Fraud reduction solutions in the supply chain are in high demand in 2024.
How do we know this? Supply chain fraud costs global businesses millions of dollars each year, with manufacturing companies losing $350 million alone.
On average, a typical corporation loses 5% of its annual revenue to fraud. These frauds manifest in myriad forms – from duplicating invoices to delivering non-compliant parts and, even more insidiously, through kickbacks from vendors.
According to Deloitte, a staggering 28.9% of professionals reported incidents of supply chain fraud within their organizations. Yet, alarmingly, 26.8% of these organizations lack any robust program to combat such risks.
But why should this concern us deeply?
Larry Kivett, Deloitte Financial Advisory, explains, “With reputational, litigation, and regulatory repercussions hanging in the balance, companies can’t afford to dismiss supply chain fraud prevention and detection. Schemes constantly evolve and come from every direction, making vigilance crucial.”
In this concerning scenario, companies are looking forward to effective fraud reduction solutions in the supply chain. This is where the recent trend of adopting Artificial Intelligence (AI) and Machine Learning (ML) to detect and prevent supply chain fraud have become important.
But how can AI/ML make a difference to existing fraud reduction solutions in the supply chain?
Compared to traditional solutions, AI/ML stands out due to its ability to automate and analyze massive amounts of data and learn to identify potential fraud patterns.
But before we delve deeper into the role of AI/ML in fraud reduction, let’s first understand why the supply chain industry is so prone to fraud.
Why is The Supply Chain Sector Vulnerable To Fraud?
The UK’s airline industry has been in the forefront of numerous scandals involving fake airline partsThe global supply chain, a complex and intricate network of multiple vendors, has increasingly become the target of sophisticated fraud schemes.
Take the UK’s airline industry, which has experienced scandals involving fake engine parts made possible by false documentation.
According to research from consultancy firm Accuracy, there has been a 13% year-over-year increase in cases of procurement fraud in the UK, highlighting a larger trend in supply chain fraud.
According to Atanu Chaudhuri, a professor at Durham University Business School, “Supply chain fraud has been around for a long time, but it is quite rampant, and it’s moving into new industries.”
This expansion into new sectors is alarming, especially as fraudsters now target component manufacturers and other industrial sectors – industries that are crucial to the safety and security of millions of customers. Chaudhuri further emphasizes the challenge of addressing this fraud, particularly due to the limited visibility companies have over their third- and fourth-tier suppliers.
This brings us to the question – what are the typical patterns of fraud found in the supply chain, and how can businesses combat them?
Typical Patterns Of Fraud In Supply Chains
Here are some common scenarios identified by insights from KPMG’s research:
- Complex Supplier Networks: Supply chains are vulnerable due to their multi-tiered structure involving direct suppliers and numerous subcontractors. This complexity makes it difficult to monitor and ensure integrity across all levels, necessitating enhanced visibility and stringent due diligence processes.
- Foreign Jurisdiction Risks: Expanding into global markets increases exposure to various forms of misconduct, such as bribery and corruption. The challenges of operating in diverse legal and cultural environments call for specialized local knowledge and robust compliance measures.
- Technological Exploits: The reliance on complex IT systems for supply chain management introduces risks of cyber fraud, including phishing and malware attacks. Strengthening cybersecurity measures and implementing advanced fraud detection technologies are critical.
- Internal Control Weaknesses: Fraudsters often exploit gaps in an organization’s internal control systems. Economic pressures leading to reduced internal controls heighten this risk. Balancing cost-cutting measures with the need for effective fraud prevention is vital.
- Strategic Risk Management: Effective risk management within the supply chain requires the identification of existing vulnerabilities while also anticipating potential future threats. This involves integrating fraud risk factors into strategic planning and decision-making processes.
AL/ML – Building More Effective Fraud Reduction Solutions in the Supply Chain
Having understood the challenges with fraud reduction in the supply chain, it is now time for us to review existing technologies that can provide effective solutions.
AI/ML is one such technology that carries a lot of potential. Let’s explore some of the popular AI/ML trends in the supply chain:
FICO’s Falcon Sets an Example for Fraud Management Tools
Have you heard about FICO’s innovative approach to fraud reduction solutions in the supply chain using machine learning?
Consider this: a manufacturer might deal with tens of millions of supply-chain purchases annually. This volume is far beyond the capacity of human risk management teams, but it is precisely where AI and machine learning can make a difference.
In industries like banking and financial services, AI has been used for fraud detection for years. Now, this technology is becoming more popular in supply chain transactions in manufacturing.
The key lies in machine-learning models, which differ from traditional programming.
Instead of following a fixed set of rules, these systems are ‘trained’ to identify patterns in large datasets, including procurement records, some of which may be tied to known instances of fraud.
FICO’s Falcon platform, a neural network-based fraud-detection model, is a great example of this approach.
Their Fraud Assurance Navigator (FAN) embeds this model into a platform that oversees procurement activities. It then seamlessly integrates with Enterprise Resource Planning (ERP) systems, which are vital in managing corporate supply chains.
These systems cover a wide array of functions, from cost analysis to production scheduling, inventory, and sales.
The AI model in FAN scrutinizes every step of the procurement process, from purchase requisitions and orders to invoice receipts. It even extends to monitoring transactions made through purchasing cards, which is a common method in procurement.
This level of detailed, automated analysis is pivotal in identifying and preventing fraudulent activities, thereby enhancing the efficiency and security of supply chains.
Advancing Fraud Detection in Supply Chains through AI Training Methods
American computer scientist Alan Perlis once said, “A year spent in artificial intelligence is enough to make one believe in God.” Perlis’ quote demonstrates the sheer power that AI is capable of.
Give an AI enough data, and it can turn those into useful insights that would require numerous human employees to deduce. This makes it especially valuable for fraud reduction solutions in the supply chain, where every error in fraud detection costs millions of dollars for businesses.
Let’s now delve into three AI training methods that are most popularly used for fraud detection in supply chains:
Supervised Models for Known Scams
- Supervised AI models are trained using sets of procurement records, including both normal and fraudulent transactions.
- The AI learns to identify specific ‘features’ indicative of fraud in these records. The range of features varies, but each plays a crucial role in the fraud detection process.
- Once operational, these models produce scores indicating the likelihood of fraud in new transactions, similar to credit scores.
- However, frauds evolve, and so must these models. They require periodic retraining with fresh data to stay effective against new variations of known scams.
Unsupervised Models for Novel Fraud Techniques
- Unsupervised models are often used to tackle unique fraud techniques. These models don’t search for predefined fraud types but develop an understanding of what typical procurement transactions look like. This makes them extremely useful for fraud reduction solutions in the supply chain.
- During the training phase, the unsupervised AI analyzes a large set of transaction records to establish a baseline of ‘normal’ procurement activity.
- Post-training, the AI looks for outliers in new transactions—deals that deviate significantly from the established norm. Transactions with high deviation scores are flagged for further investigation.
Personas and Networks in Machine Learning
- Some AI models go beyond analyzing transactions individually and employ personas—complex psychographic archetypes—to group purchasing agents.
- These models score transactions based on what is normal for a particular persona rather than an individual, making the process more efficient for large datasets.
- Emerging AI techniques in fraud reduction solutions in the supply chain include cognitive computing, which can identify and map networks of fraudulent activity, recognizing links between purchasers and known fraud actors.
Strategies for Mitigating Fraud in the Supply Chain Industry
While it is clear that AI/ML is making a positive difference in enhancing fraud reduction solutions in the supply chain, there are other parallel strategies that companies can enforce.
These include internal measures to combat supply chain fraud as well as addressing some of the most common external causes of fraud, such as cargo theft.
Addressing Cargo Theft: A Major Challenge in Supply Chain Management
Cargo theft is a leading cause of supply chain fraud and has increased by over 57% between 2022 and 2023Cargo theft is a significant and high-profile issue in the supply chain industry. According to CargoNet, there were over 880 incidents of cargo theft in the United States and Canada in 2015 alone, with reported losses totaling $98 million.
That percentage has increased by over 57% in 2023 vs. 2022, according to recent reports.
This type of theft often involves the pilferage of goods from various points in the transport process, including storage facilities, vehicles, and distribution centers.
The implications of cargo theft are vast, disrupting manufacturing processes, skewing customer perceptions, and negatively impacting sales channels. The emergence of stolen goods in black markets can further damage brand reputation, especially if products are mishandled.
Fortunately, AI/ML can massively help companies combat cargo theft:
- Enhanced visibility of cargo: Traditional cargo security methods are notoriously ineffective due to limited visibility. Boosting it with AI/ML can ensure that the security systems are updated with the most recent information and can perform multiple security scans at every checkpoint. This gives companies control over a cargo’s movement at every stage.
- Intelligence and AI-powered insights: Businesses can benefit from using AI/ML powered systems to create detailed reports that provide businesses with key insights on the performance of their cargo shipments, largest causes of fraud, as well as common weaknesses in the existing systems.
Internal Company Measures to Combat Supply Chain Fraud
Supply chain companies need to enforce guidelines to safeguard against fraudulent activitiesTo effectively manage and reduce the risk of fraud in the supply chain, organizations must adopt a holistic approach that encompasses prevention, detection, response, and ongoing program maintenance. Here are some key methods:
- Due Diligence on Third-Party Suppliers and Vendors: Conduct thorough background checks on suppliers, assessing their compliance, financial stability, manufacturing capacity, and potential conflicts of interest.
- Segregation of Duties: Ensure that no single individual can process a complete transaction without oversight. This approach minimizes opportunities for misconduct in manual data input and transaction processing.
- Regular Risk Assessments: Identify current risks and evaluate the adequacy of existing controls. Regular assessments help adapt strategies to evolving threats and control weaknesses.
- Employee Training and Awareness Programs: Develop comprehensive training programs that are tailored to specific job functions and risks identified in assessments.
- Developing Incident Response Plans: Prepare for fraud incidents with a structured response plan involving key departments like Finance, Legal, IT, and HR.
- Utilization of RFID Technology: Radio Frequency Identification tags can be affixed to goods for better tracking and inventory control, significantly aiding in fraud prevention.
The Future Of Fraud Prevention In The Supply Chain Industry
“Products can be easily copied. But a supply chain can provide a true competitive advantage.” Author and researcher Yossi Sheffi hit home with this quote.
In a global supply chain where manufacturers from all over the world can supply finished products to US brands, the effectiveness of a company’s supply chain is more important than the product itself.
The judicious use of fraud reduction solutions in the supply chain and implementing the latest AI/ML trends can mean that companies can save costs, run more efficient supply chains, and deliver customer satisfaction better.
Many companies are also shifting their business models to blockchain, where supply chain transparency and traceability are helping businesses slash costs and increase visibility.
For instance, the use of advanced tracking systems, AI, and blockchain can significantly amplify the ability to monitor the flow of materials and authenticate their origins and quality. This can bring down supplier fraud and ensure all products are legitimate and sourced directly from the manufacturers.
The outlook is clearly positive. A future where every step in the supply chain is instantly visible and transparent, with limited opportunities for fraud. So there is nothing more to worry about, right?
Not exactly. AI/ML and blockchain are very effective in theory but lack enough exposure to real-time fraud scenarios due to their recent implementation.
There is a very high probability that fraudsters will also use AI/ML to exploit company systems. This makes AI/ML implementation and scalability extremely important for companies that are looking to steer clear of fraud and cargo theft.
However, amidst this conversation – one thing is quite certain. AI/ML is here to stay and will become an extremely important addition to existing fraud reduction solutions in the supply chain industry. Already, AI in supply chain management is projected to reach $10 billion by 2025.
Can we eventually hope for a fully autonomous supply chain with robots working alongside human employees and ensuring 100% success? That day may not be too far away.
FAQs
How is AI used in supply chain?
AI plays a crucial role in optimizing the entire supply chain, from predicting demand and inventory needs to automating logistics and managing complex routes. It analyzes vast amounts of data, identifies patterns, and provides real-time insights to improve efficiency, reduce costs, and enhance customer satisfaction. AI-powered tools help companies make smarter decisions, adapt to changing market conditions, and optimize their operations for maximum profitability.
What is the future of AI in supply chains?
The future of AI in supply chains is bright, promising to revolutionize operations. AI will optimize logistics, predict demand fluctuations, and personalize customer experiences. By automating tasks, AI will free up human resources to focus on higher-level activities, leading to greater efficiency and agility in navigating the complex global supply chain landscape.
Which companies use AI in supply chain?
Many companies across various industries are leveraging AI in their supply chains. From logistics giants like FedEx and DHL optimizing routes and delivery times, to retail giants like Amazon and Walmart predicting demand and managing inventory, AI is transforming how goods move from origin to customer. Even smaller businesses are adopting AI-powered solutions for tasks like procurement and risk management, making the technology accessible to all.
What is GenAI in supply chain?
GenAI, or Generative AI, in supply chain uses cutting-edge AI models to analyze vast amounts of data. This allows for intelligent forecasting, demand planning, and even automated decision-making. It can predict disruptions, optimize routes, and streamline inventory management, leading to significant improvements in efficiency and cost savings.
How Zara uses AI in supply chain?
Zara leverages AI to streamline its supply chain, making it one of the most efficient in the fashion industry. AI algorithms analyze customer data and sales trends, enabling Zara to forecast demand accurately and adjust production accordingly. This allows them to reduce waste and keep their inventory lean, while simultaneously ensuring they have the right products available at the right time.
Does Amazon use AI in supply chain?
Yes, Amazon heavily relies on AI to optimize its complex supply chain. From forecasting demand and predicting stockouts to automating warehouse operations and optimizing delivery routes, AI plays a crucial role in ensuring efficient product flow and customer satisfaction. This includes machine learning algorithms analyzing vast datasets, predicting customer needs, and streamlining warehouse processes for increased efficiency.
How is AI used in logistics?
AI is revolutionizing logistics by automating tasks and optimizing processes. From route planning and delivery scheduling to warehouse management and demand forecasting, AI algorithms analyze vast amounts of data to improve efficiency, reduce costs, and enhance customer experience. By leveraging machine learning and predictive analytics, AI helps logistics companies navigate complexities and optimize their operations for better performance.
How AI can forecast demand in supply chain?
AI can forecast demand in the supply chain by analyzing vast amounts of historical data, identifying patterns and trends that humans might miss. This includes internal data like past sales and inventory levels, as well as external factors like economic indicators and social media trends. AI algorithms can then generate accurate demand predictions, helping businesses optimize production, inventory, and logistics for better efficiency and profitability.
What is the risk of AI in supply chain?
AI in supply chains presents risks like over-reliance on algorithms, potentially leading to blind spots and vulnerabilities in decision-making. It also raises concerns about data privacy and security, as AI systems require vast amounts of sensitive information to function. Finally, job displacement is a potential risk as AI automates tasks currently performed by humans.
How can AI make supply chains more sustainable?
AI can revolutionize supply chain sustainability by optimizing logistics and resource allocation. Through predictive analytics, AI can anticipate demand fluctuations, minimize waste, and optimize transportation routes, reducing emissions. By analyzing real-time data, AI can identify and eliminate inefficiencies, leading to reduced energy consumption and improved resource utilization.
How can AI help in procurement?
AI can revolutionize procurement by automating tedious tasks like data entry and vendor research, freeing up your team to focus on strategic sourcing. AI-powered tools can analyze vast amounts of data to identify cost savings and predict supply chain disruptions, leading to more informed decision-making. Furthermore, AI chatbots can streamline communication and provide 24/7 support for suppliers and stakeholders.
How AI can help in inventory management?
AI can revolutionize inventory management by automating tasks, optimizing stock levels, and improving forecasting. By analyzing historical data, AI can predict demand fluctuations and recommend optimal ordering quantities, reducing stockouts and excess inventory. This leads to significant cost savings and improved efficiency in supply chains.
