Updated November 22, 2022
The fraud risks that organizations face are more dynamic and persistent than ever, according to the 2022 Anti-Fraud Technology Benchmarking Report from the Association of Certified Fraud Examiners (ACFE). But gone are the days when you could manually parse through documents to uncover fraudulent transactions. Companies are overwhelmed by data, and next-level fraud detection is now achieved with artificial intelligence (AI) software, which imitates human intelligence to perform tasks, and machine learning (ML), an AI application in which a machine uses data to learn on its own and improve its performance based on experience.
Not surprisingly, the use of AI and ML in anti-fraud programs is expected to more than double over the next two years, according to the ACFE report. Below we cover why organizations that incorporate AI and ML into anti-fraud programs will be better equipped to detect and deter fraud — including a look at why this is especially important after disruptions such as the COVID-19 pandemic — plus how you can take the first steps toward using AI/ML for your own fraud prevention program.
The COVID-19 pandemic caused great stress for organizations, customers and employees alike. As companies struggled to stay afloat, business leaders often had to put fraud prevention and detection measures on the back burner, creating the perfect storm for increased fraudulent activity. For example, pivoting to a remote workforce created the potential for more unauthorized employee access to sensitive data, and expediting new suppliers where supply chains were cut off often resulted in less time spent performing vendor due diligence.
In the ACFE's The Next Normal: Preparing for a Post-Pandemic Fraud Landscape report, 45% of organizations surveyed said they believe that enhanced technology for anti-fraud programs is necessary for making programs more effective post-pandemic, and 43% expected an increase in anti-fraud program budgets from 2021 to 2022. While data analysis techniques such as anomaly detection, automated monitoring of red flags and data visualization lead the way in fighting fraud, AI and ML continue to gain momentum, with 26% of organizations reporting they expect to adopt this technology in the next one to two years.
These survey results make sense considering companies have become more familiar with the increased fraud risks and challenges arising from the pandemic. Disruption always presents opportunities for fraudsters to take advantage of the chaos, and it's important to understand the benefits of AI and ML so that you can make informed decisions on what applications would work best for your fraud prevention program.
Here are some of the benefits of leveraging anti-fraud AI and ML platforms for your business.
More than ever, companies need insight into how their data can be used to detect and deter fraud and the financial losses that come with it. With AI, companies can train their data analytics programs to recognize red flags in real time as transactions occur.
AI and ML can scour through colossal data sets, or "big data," to identify irregular or suspicious transactions in a fraction of the time it would take to apply traditional data analysis techniques. Big data could mean petabytes or exabytes of data, billions to trillions of records — volumes of information that are almost unfathomable. According to the ACFE benchmarking report, 64% of companies said that data analytics were very beneficial in analyzing large volumes of data, and 60% said analytics were very beneficial in allowing them to detect anomalies both more quickly and more efficiently.
Companies collect data from a number of sources. In many instances, internal data is not sufficient and must be aggregated with external data (public records, government watch lists, social media, third-party data, emails, images, etc.) to provide valuable insights for decision makers. AI can help experts navigate the process of linking information from various sources, departments or systems to build a complete picture that contains accurate and reliable information.
According to the ACFE Occupational Fraud 2022: A Report to the Nations, average losses from fraud schemes more than double if the duration lasts longer than 24 months. With the advent of AI, companies can now focus on the deterrence of fraud rather than having to jump into crisis mode after a fraud occurs, and tailor their analytics to produce results that are truly meaningful.
For example, AI can point to suspicious data where you can apply resources for further investigation, flagging items such as certain keywords used in emails, transaction activity outside of regular business hours, or the address of a new vendor that is zoned as residential. Early identification of these and other threats mitigates the potential fraud risk and can significantly reduce the window available for fraudulent activity to occur.
Before AI, a fraud investigation was a time-intensive task that required combing through every individual transaction. When used properly, AI can save countless hours and reduce human errors by automating tedious, manual processes. Ultimately, this enables employees to focus their efforts on more strategic activities.
There will always be a human element to fraud detection, however, and AI is only as good as the team behind its design. If humans are fallible, then the "intelligence" designed by humans is also fallible. Monitoring your AI tools for efficacy over time is not only smart, it is a protection of your investment.
Identifying an appropriate AI project begins by considering your company’s overall strategy and objectives. What goals are driving your business? What pain points exist in terms of time and cost? Finding the connection between current obstacles, your business strategy and the AI application will help illuminate potential use cases.
Next, determine if AI makes sense for your organization by discovering whether you have the accurate, consistent and accessible data needed for the technology to work. Start by cataloging your data assets and establishing a data inventory. Consider what data within the inventory is relevant. Experiment with the relevant data and fail fast. The quicker you can identify the wrong approaches, the sooner you can discover the correct approach.
If project challenges using existing data are insurmountable, devise a data collection plan to expand your inventory. Alternatively, seek third-party data sources to augment your project’s data set. Although more data is often beneficial, the right data for a project is often the deciding factor between success and failure.
Fraud risks continue to increase, and more and more companies are beginning to explore AI as they realize its many benefits for anti-fraud programs. With the right data and strategic plan, you can take the first steps to leverage AI to gain an advantage in fraud detection and prevention and better protect your business in years to come.
To learn more about how to deploy the right AI programs and reach your AI goals, explore Armanino Intelligence.
