Identifying ATM Skimming & Other Suspicious Behavior with Intelligent Video Analytics
A recent headline in a major U.S newspaper contained the words no bank or credit union ever wants to hear: “Theft of debit card data from ATMs soars.”
The article described alarming new statistics from credit scoring agency FICO, which shows that theft of card data at U.S. ATMs has surged recently, reaching its highest level in 20 years.
According to the article, theft of debit card data from bank ATMs jumped 174% from January to April of this year, compared to 2014. Attacks on debit cards at non-bank ATMs were even more prevalent, rising by 317% over the same time period.
This kind of fraudulent activity has a two-fold effect on banks and credit unions: it shakes consumer confidence, but also results in major financial losses.
Massive Skimming Schemes Defrauding Banks
Take the recent case of one ATM skimming scheme in the U.S. A group was installing card-reader devices and pinhole cameras at ATMs in several states. They’d use the stolen data to create fake debit cards and then withdraw cash from victims’ accounts. This case alone cost several major financial institutions $5 million.
And the problem is only getting worse. Reports, including this 2014 research paper, note how skimmer technology is evolving and becoming more difficult to spot. Skimmers are now for sale online with enough memory to store data from hundreds of stolen cards.
This kind of sophisticated criminal activity demands an equally sophisticated security solution.
Intelligent Software That Does the Work for You
Video surveillance is one powerful tool banks can use to safeguard their ATMs. Until recently, however, monitoring video could be a time consuming task; it’s not realistic to watch an entire day’s worth of video to locate one 10-minute incident.
What’s needed is a way to identify when someone is at an ATM, but not actually making a transaction.
New intelligent solutions integrating surveillance video with transaction data and analytics have made that possible. They work by identifying when someone is at an ATM through video analytics, and also identifying when that same person is not making a transaction. The latter information is determined by integrating the solution with the bank’s central transaction server.
Retailers can also benefit from these same solutions.
Let’s look at some specific examples of how these solutions work in both banking and retail environments:
Scenario 1: A fraudster is installing a skimming device onto a bank’s ATM. The next customers to use the machine will be victimized by this crime.
Intelligent Video Solution: By using the right type of video analytics, this bank can capture images of each person who uses the ATM, and receive an alert when someone has been standing at the machine. A video-based business intelligence solution that’s integrated with a central transaction server allows a bank to set its system to search for instances where someone is lingering at the ATM and no transaction is taking place – a sign that someone could be installing a skimming device. The system automatically triggers an alert in these cases, and lists all instances fitting this description in a report along with other details like the date, time and location. A security officer can very quickly investigate the video and corresponding report, and if need be, inspect the ATM to see if it’s been tampered with.
Scenario 2: A thief has created multiple debit cards using stolen data he’s found online. He’s now using the cards at an ATM to withdraw money from other people’s accounts.
Intelligent Video Solution: Similar to the above solution, this bank can use its intelligent video to proactively search for instances of the same person making multiple withdrawals with different cards, which could signify theft. Again, the bank’s video surveillance captures images of all ATM transactions, while its video analytics detect when a person is standing at the machine. With a customized alert in place, the system compares transactions with video records, and automatically flags instances where the same person remained at the ATM and conducted multiple transactions with different cards. The system conveniently lists all these transactions in an easy-to-read report, helping security officials rapidly locate instances of cash harvesting.
Scenario 3: An employee at a retail clothing store falsifies a customer return in order to steal the value of an expensive piece of clothing. The employee waits until no one is near the point-of-sale (POS) to do the false return, and then pockets the cash.
Intelligent Video Solution: By integrating its POS and video systems, and using the right analytics over its POS, this retail business can quickly search for transactions that take place when no customer is present at the POS. This is possible because the analytics detect when there is no activity or motion in front of the POS, where a person should be standing. If the system is configured to compare that data with transactions, it will automatically generate alerts when transactions are taking place and no customer is detected. A manager can quickly scan video of each instance to verify what took place.
Surveillance systems that integrate video and analytics with transaction data in the right combination can offer unique solutions to banks, retailers and other organizations, helping them identify suspicious behavior and rapidly detect fraud. Solutions like March Networks Searchlight also go beyond loss prevention and security to deliver valuable insights into customer service, employee behavior, marketing efforts and more. Remember to do your research, learn what comparable businesses are using and ask for a demonstration to learn more.