Virtual cards—digital versions of physical credit or debit cards typically used for online transactions or recurring payments—offer a powerful opportunity to streamline operations while enhancing security. When combined with the power of AI, virtual cards provide a safe way for individuals both inside and outside of the organization to purchase the goods and services they need. Enhanced Internal Control The success of virtual cards lies in their ability to provide targeted, controlled spending.

Unlike traditional company-paid credit cards, virtual cards are issued with a specific intended use or purchase scenario. This could be for a single purchase or a series of purchases. When a virtual card is issued, it is configured with built-in internal controls tailored to its specific purpose. Any purchase made with this card must adhere to these controls; if it doesn’t, the transaction is declined at the point of sale.

Controls can include spending limits, effective date ranges, and merchant restrictions based on the merchant’s name or category. For example, Mike, a construction manager at a commercial construction company may need to buy materials or tools while on-site. Mike could be issued a virtual card with a merchant category control that limits purchases to suppliers of construction materials or tools. To maintain budget control, the card might also have a spending limit and an effective date range specific to a particular job.

These enhanced internal controls reduce the risk of fraudulent spending, as cardholders are restricted by more than just the company’s overall credit limit—they’re bound by targeted constraints that align with the card’s purpose. AI Helps Further Reduce Fraud Exposure While enhanced internal controls significantly reduce fraud risk, certain vulnerabilities remain. AI plays a crucial role in addressing these gaps. Take Mike’s virtual card purchase, for example. He might buy thousands of dollars’ worth of materials from a home improvement store but hidden among the legitimate items is a $500 gift card for himself.

The transaction meets all the internal controls: It’s at a valid merchant, within the spending limit and occurs during the allowed date range. However, the fraudulent purchase is concealed within the receipt’s line-item details. This is why receipts must be submitted with full line-item details.

Only by auditing these details can fraudulent spending be detected. AI can be instrumental in discovering potential fraud like this. The methods it uses depend on whether the receipt is submitted as an image or as data. Detecting Fraud in Receipt Images Fraudsters sometimes create fake or altered receipt images.

For this type of situation, AI uses several methods to detect fraud: Pixel-level analysis: AI can analyze individual pixels to identify inconsistencies in texture, lighting, or noise patterns. Edited portions of an image often have different pixel characteristics compared to unaltered parts. Machine learning: Machine learning algorithms can be trained on a large dataset of authentic and altered receipts to recognize patterns specific to genuine receipts from specific merchants. Deep learning and convolutional neural networks (CNN): Deep learning models, particularly CNN, are highly effective in detecting image alterations by identifying patterns invisible to the human eye. Shadow and reflection analysis: AI can analyze the natural shadows, reflections, and lighting present in a receipt image. When a receipt is digitally altered, these features may become inconsistent with the rest of the image. Detecting Fraud in Receipt Data Receipts can also be submitted as data, either directly from online purchases or converted from images using AI-powered optical character recognition (OCR).

AI analyzes this data for potential fraud by: Anomaly detection in spending patterns: AI systems can analyze large volumes of receipt data to detect unusual or unexpected spending patterns. Duplicate receipt submission detection: AI can detect when the same receipt is submitted multiple times, either accidentally or fraudulently. Cross-referencing with external data: AI can verify the authenticity of receipt data by cross-referencing it with external databases. Fraudulent modifications in amounts or items: AI can detect subtle changes in amounts or item descriptions that may indicate fraud. In our gift card example, AI can identify when expensive items are falsely itemized under allowable categories, such as labeling personal electronics as office supplies. A Safer Path Forward The combination of virtual cards, which inherently provide enhanced internal controls, and AI-driven receipt fraud detection offers operational managers a powerful tool for safeguarding purchases. Built-in safeguards like transaction limits, vendor restrictions and real-time monitoring make it harder for unauthorized expenses to go unnoticed.

In an environment of ever-increasing ways in which bad actors are committing fraud, AI-powered virtual cards not only reduce the risk of fraudulent spending, they also allow organizations to modernize their financial operations in new and secure ways. 0 SHARES 0 VIEWS Share on FacebookShare on TwitterShare on LinkedIn

By john toman
Nov 11, 2024 00:00
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