Welcome to the second in our series of blog posts on the impact of AI in accounts payable (AP). Previously, we explored how AI-powered data analytics is helping AP teams plan for the future and optimize their current performance. This time, we’re looking at how artificial intelligence can help teams detect and prevent fraud.
Featured Resource: How to Leverage Artificial Intelligence in Your Accounts Payable
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Understanding the problem
Fraud is a growing problem. More than half (51%) of surveyed businesses experienced fraud in 2022, and cybercriminals are using increasingly sophisticated techniques to evade detection — sometimes, even utilizing artificial intelligence themselves. As such, it’s becoming harder and harder to manually detect and prevent fraud. Fraud is an arms race, and finance departments need to protect themselves with the latest technologies or risk becoming increasingly vulnerable. This quote from Forbes sums up the challenge:
"Traditional methods of investigating financial fraud rely on manual verification and human analysis. Unfortunately, these methods often fall short when dealing with today's increasingly complex and sophisticated fraud schemes. These shortcomings make it imperative to explore the transformative potential of artificial intelligence and machine learning for revolutionizing financial fraud investigations."
- Forbes
AP teams are particularly susceptible to fraud, as attacks can stem from both internal and external sources. Let’s explore some of the fraud vulnerabilities within the accounts payable function:
- Invoice fraud: this common form of fraud involves submitting highly-convincing fake invoices, or altering the payment details of legitimate ones. Invoice fraud often succeeds because human AP professionals find it difficult to tell a fraudulent invoice from a real one.
- Duplicate payments: duplicate payment can occur by accident in an overworked AP department, or one that’s highly dependent on manual processes. However, fraudsters can exploit these vulnerabilities by submitting multiple invoices for the same transaction.
- Phishing & social engineering: scammers impersonate vendors or colleagues to manipulate AP professionals into changing payment details — again, exploiting human vulnerabilities.
- Expense abuse: internal employees can exploit a company's expenses policy for personal gain.
The problem is, most instances of AP fraud are extremely difficult to spot. They're hard to detect at the time, and even harder to detect after the fact — as this quote from Towards Data Science indicates:
"Internal and external audits, while often thought of as a pivotal tool for uncovering fraud, are only responsible for 14% and 3% of fraud detection respectively. One of the problems auditors face is finding a metaphorical needle in a haystack: searching for red flags among thousands of legitimate journal entries."
- Towards Data Science
AI fraud detection and prevention
Artificial intelligence excels at many of the tasks humans are bad at: quickly analyzing large volumes of data, recognizing patterns, trends, and anomalies, and doing so tirelessly and with 100% accuracy. They're perfect for finding that "needle in a haystack". Machine learning models can be 'trained' to recognize normal patterns, processes, and behaviors — and conversely, their opposites. Anything that falls outside of these parameters is flagged for review, helping teams cut out fraud at source.
Here are some of the processes involved in AI fraud detection:
- Data analysis: the AI system is 'trained' on legitimate transactions using historical data: invoices, purchase orders, receipts, etc. This establishes rules and norms to help spot potentially fraudulent behavior.
- Anomaly detection: by continuously monitoring transactions, machine learning algorithms can identify anomalies and deviations from established behaviors to flag fraudulent activities.
- Pattern recognition: AI systems can recognize unusual patterns — such as duplicate payments, changes to payment details, or new vendor information — and flag them as potentially suspicious.
- Predictive modeling: over time, AI can assume a more active role in predicting potentially fraudulent activities — learning from historical data and past instances of fraud to enable teams to 'retaliate first'.
- Automation: AI systems allow for complete or partial automation, where the system takes an action itself: such as flagging for manual review, blocking a payment or change of vendor details, or initiating an investigation.
Quadient AP by Beanworks is built on artificial intelligence. Our AP automation solution provides real-time visibility into the accounts payable process, so teams can quickly identify and address potential risks. It also helps businesses comply with regulations and internal policies by maintaining accurate records and providing detailed audit trails.
Keep an eye on our blog for more in our series on the use of AI in accounts payable.
Featured Resource: How to Leverage Artificial Intelligence in Your Accounts Payable
