What is Manufacturing Fraud?
These days, the manufacturing industry is more complex than ever before. Globally distributed supply chains, inventories, logistics, and increasingly dynamic manufacturing processes have all brought tremendous opportunities. However, these new processes have also created weak spots for vulnerabilities to creep in. One of the biggest challenges for the manufacturing industry is addressing the risks of fraud. Fraud can come in many forms, including:
Procurement and Inventory Schemes
Conflicts of Interest
Theft or Misuse of Inventory
Price Fixing or Bid Rigging
As supply chains have gotten broader and terribly complex, maintaining visibility at all times has become increasingly difficult. Furthermore, as organizations continue to rely on more and more third-party vendors for a variety of services, the need for vendor monitoring has increased. On average, manufacturers lose almost EUR 200,000 every year, according to 2020 Report to the Nations published by the Association of Certified Fraud Examiners (ACFE). That's significantly higher than the median fraud loss for all industries (EUR 150,000). (Miller, 2020)
In the current environment, the question is not if, but when. Your organization needs to be prepared to address and mitigate fraud. This is a risk that you cannot afford to ignore. Fortunately, with big data, analytics and artificial Intelligence we have the power to effectively combat fraud. The answer is anomaly detection.
AI-Powered Anomaly Detection
Most manufacturers now have large numbers of sensors spread throughout their facilities. These sensors monitor supply chains, processes, and even specific products as they move from raw material to finished items. Furthermore, many organizations still have employees manually recording various information into their systems. The thousands of data points created by these sensors and data inputs result in massive volumes of big data.
Over time, this data will form patterns and trends that define the normal operations of your company. Anomaly detection works by comparing your real-time data streams to these “normal” patterns and highlighting any inconsistencies or outliers as they occur. But there’s a catch. It is humanly impossible to monitor all of these complex data streams and manually track ever incident or outlier. That’s why we have developed artificial intelligence solutions, tailored to industrial use cases.
With the help of machine learning and automation, anomaly detection systems can automatically analyze large data sets and alert you to deviations. Sometimes these anomalies can be explained; other times, they may indicate fraud.
How Anomaly Detection Combats Fraud
AI-powered outlier detection systems rely on machine learning techniques like Deep Learning to detect anomalies. Anomaly detection can be used for a wide variety of purposes, including fraud and intrusion detection. This kind of analysis can also help you improve your business processes and uncover new opportunities for growth. Because it is automated, detection systems can run 24/7 and monitor all aspects of your organization.
Furthermore, the AI component identifies true anomalies from ordinary deviations. This is critical. Cognitive computing helps the anomaly detection system to use self-learning algorithms powered by data mining, pattern recognition, and natural language processing. Over time, the accuracy, and precision of your detection system increase because it has the capability to learn more about your organization’s data.
You may use anomaly detection to discover quality issues early, and you can triage any incidents quicker. In today’s multi-layered manufacturing environments, Excel sheets are not a practical tool for managing and monitoring your data anymore. With increasingly intertwined supply chains, multilayered inventories, and frequent, large transactions, you need a way to gain visibility into your organization’s internal controls, records, and manufacturing procedures.
Many tools may give you the technical capabilities to identify oddities, but will often require you to manually identify them hidden in layers of dashboards and reports. Instead, machine learning can automate fraud detection to highlight red flags such as:
Unusual Inventory Reductions
Split Purchase Orders
Unexplained Rise in Invoice Volumes
Repeat Payments To Vendors Without Services Rendered
Abnormal Bid Prices
Sudden Rise in Customer Complaints
Anomalies can occur at a variety of different levels and points and they can indicate fraud, which is why it is critical to monitor them. The data you collect using an AI-powered anomaly detection solution can also help you build a fast-response plan reacting to such incidents.
Constructing A Robust Fraud-Response Plan
The data outputs and insights provided by your anomaly detection system can help you to build a data-driven fraud response plan. A response plan can be broken down into four main categories:
Next, let’s break down these three main elements in further detail and explore a practical anomaly detection workflow.
Step #1: Detect
Finding unusual behavior or anomaly is always the first step in responding to fraud. With our solutions, you can conduct automated signal analysis to understand and categorize trends, seasonalities, and thresholds for what is considered an anomaly. It is critical to set up your analysis tracking system at an appropriate level of granularity.
You need to acknowledge the unpredictability of your industry while not getting too far ahead from ground-level details. If you are only monitoring at a high level, you are likely to miss vital anomalies that could cost you. However, if you track in too granular a fashion, you’ll end up with a storm of alerts. Most of these alerts will not be real issues, but just the general variances that happen from time to time. This can cause a phenomenon known as “alert fatigue”.
When alert fatigue sets in, your employees no longer pay attention to alerts when they come in, and your organization is left vulnerable as a result. This is why it is critical to maintain control of your monitoring levels as part of your response plan. Anomaly detection makes it easy for you to monitor at the relevant levels for your business and industry and prevents your team from drowning in overengineered processes.
Step #2: Notify
Once your system detects an anomaly, the impacted users and people need to be informed. Often, incidents will occur outside normal working hours, which is why a robust notification system needs to be in place as the main component of your response plan. Alerts should be delivered across multiple channels, including email, Slack, Teams, Webhooks, and any other communication platform or channel that fits into your team’s existing workflow.
The best anomaly detection vendors provide you with a dashboard and custom interface for rapidly displaying the insights and information you need to resolve situations. Here at craftworks, we develop software dashboards alongside our custom anomaly detection solutions to ensure that you have a platform that suits your particular needs.
Messages from your system should provide a clear assessment of what has happened and include additional useful information that will help your team decide if an event should be investigated further
Step #3: Explain
The third step of your manufacturing fraud plan should be to explain the anomaly and then develop a way to resolve the problem. The usual question to ask is: “why has performance changed for this metric?” In this phase of the plan, your organization will need to do a root cause analysis to answer this question and address any incidents. With a data analysis platform, you are able to quickly pinpoint any information that may be related to this deviation.
Once you have identified the root cause, you can then take steps to address the issue. This is why it is so crucial to record and analyze your data. You cannot find the root cause of a change in an evidence-based way unless you have data-driven metrics at your disposal. These measurements give you visibility and insight into what exactly is going on throughout your organization. On the other hand, it is also important to remember that the incident may be caused by external factors or business contexts that are not always measurable by sensors and tracking efforts.
Root cause analysis can be automated, saving you time and money. Let our experts, help you find exactly what caused an anomaly in seconds without resorting to any kind of manual analysis. This is the power of AI and machine learning.
Step #4: Respond
Once you have identified the root cause of the anomaly, you can then respond to the incident. You will often be able to identify the source of the fraud before serious damage is induced. The system gives you the needed evidence to follow suit with any corrective actions against fraudulent behavior stemming from your supply chains, and thereby restoring the integrity of your processes.
Once you have addressed the incident, it’s essential to review what has occurred. Anomaly detection solutions provide the data to help guide your efforts towards closing any loopholes responsible for the problem. One of the core principles of security is continuous improvement. By seeing incidents as opportunities for learning, you ensure that you are in a better position to mitigate fraud risks. In addition to relying on your system, you will also need to review your awareness programs and procedures.
The Practical Use Case
Anomaly detection solutions are used in any use cases where things are not adding up - from detecting material imperfections to fighting against fraud. Through customizing your solution to your real needs, you can spot revenue leakage, system outages, and any other outliers that may be impacting profit. One of the best ways to see how something works is through examples. For us, these solutions are not theories. They are actually determining our customers’ successful growth.
One of our recent clients is a manufacturer of surfaces and coverings. Like many other organizations, they had a plethora of data streams, but had no way of storing or analyzing them. They recognized that this lack of visibility was a critical issue for them, so they reached out to craftworks. We helped them create a cloud-based data processing system in Microsoft Azure. The client was able to see the following results directly as a result of implementing their new anomaly detection system:
50% fewer errors during production
Ability to leverage real-time quality data in the manufacturing of the product
These results clearly demonstrate the real benefits of a robust anomaly detection system.
The craftworks Difference
We have personally seen how technology has the power to help organizations achieve excellence. This is at the heart of everything we do at craftworks. We will come alongside you as a partner to provide you with the guidance you need to understand how and why an anomaly detection solution would be appropriate for your particular use cases. Then, we plan and execute in-depth feasibility studies. Based on these studies, our team will work with you to build prototype anomaly detection algorithms to demonstrate the true business value of a complete system. After the solution is complete, we will then work closely with you to ensure that the platform is seamlessly integrated into your existing IT infrastructure.
We provide a variety of solutions and services when it comes to anomaly detection for your organization, including consultancy, co-development, and full-service packages. Our consultancy services include tailor-made workshops that will help you find the perfect solution for your company. Our co-development offering is targeted at organizations with a functional team in place, but that may be short on technical knowledge or experience. We are more than just a vendor; we’re the industrial AI partner for anomaly detection solutions. Are you looking for a complete solution for your specific needs? craftworks also offers a full-service package where our experts handle the entire process from beginning to end.
With the rise of Industry 4.0, data is continuing to grow in both volume and unpredictability. Anomaly detection systems have been proven to be an excellent solution for both addressing fraud and uncovering ways to improve your organization’s processes in order to increase efficiency. Using artificial intelligence, you can move from being reactive to having a more proactive posture. Technological algorithms can now provide predictive capabilities. The solutions are here today to help you take control of your organization’s data and minimize profit loss.
Miller, C. (2020, November 10). Fraud is Fraught in Manufacturing: Tips to Prevent Expensive Losses. Concannon Miller. Retrieved November 17, 2022, from https://blog.concannonmiller.com/4thought/fraud-is-fraught-in-manufacturing-tips-to-prevent-expensive-losses