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AI Might Detect SNAP Benefits Fraud

SNAP Benefits
SNAP Benefits; Source- WTXL

Over $113.9 billion in SNAP benefits were delivered to roughly 22 million households nationwide in 2022 alone. This amount marks an increase of approximately $40 billion from 2020 and more than five billion dollars from the previous year. Unfortunately, a growing portion of these payments have been taken by thieves, from lone criminals to organized crime rings, as the SNAP distribution has expanded. Food stamp fraud and theft are in increasing numbers, mostly as a result of the “skimming” of the EBT cards that are used to cover the cost of SNAP purchases.

SNAP Benefits

SNAP Benefits; Source-AS USA

Skimming Of EBT Card

When someone else’s EBT card is skimmed, fraudsters employ a device to acquire the card number including PIN. The cards have the same functionality as debit cards but lack the built-in security features of bank-issued credit or debit cards. Per Ali Solehdin, the Chief Product & Strategy Officer at INETCO, a provider of cybersecurity and fraud detection software, artificial intelligence is one potential answer to the SNAP fraud issue. Government entities and payment processors can identify and prevent fraudulent EBT transactions caused by card fraud and account takeovers by using the company’s BullzAI technology.

Solution To The Fraud Of SNAP Benefits

Each year, card theft and other types of fraud steal billions of dollars worth of SNAP/EBT benefit payments from their intended receivers. Due to the abundance of data and fraudulent account details available through avenues like the Dark Web, fraudsters are also able for stealing EBT funds via control of accounts as well as to card skimming. Restructuring the SNAP/EBT scheme to make sure chips are present on all benefit cards is one possible solution. Although this idea is now under consideration in several states, implementation would probably take years.

AI Can Prevent SNAP Benefits Fraud

In the best-case scenario, this may be accomplished in two years, but during that time, regions could still be subject to an additional $9 billion in EBT fraud losses. A preferable option is for governments and their SNAP/EBT supplier partners to “rapidly deploy” fraud protection technology that is supported by machine learning and AI that can detect irregularities in real-time. Machine learning models powered by AI can build unique profiles for every EBT card. The systems can then use behavioral analytics to instantly identify fraudulent EBT card activity that has taken place.

Without the need for data scientists or high levels of technical proficiency, this system can also teach itself to further develop as time passes in identifying fresh tactics and different kinds of payment fraud. This is a much more effective way to identify, prevent, and mitigate SNAP/EBT fraud than substituting millions of reimbursement cards or conducting sting operations to apprehend offenders, both of which are quite expensive and call for a lot of manpower. The fact that fraud detection systems supported by machine learning and artificial intelligence may be implemented in just 60 to 90 days is another advantage.

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