Rippleshot Blog

When a data breach occurs, the full impact of the incident usually takes a few months to realize the potential impact. This has certainly been the case for the Equifax breach that was first discovered last summer. This week, the credit reporting agency announced that another 2.4 millions Americans were discovered to have been impacted by last year’s breach.

This is the second time the company has announced more affected consumers, bringing the estimated total impacted to roughly 147.9 million Americans. This is the largest data breach of personal information to date.

Since the fallout of the Equifax breach, the chatter in the financial services industry has been all about identity fraud. The same sentiment was echoed in a recent study by Javelin.

According to that study, for the first year ever, data breaches compromised more Social Security Numbers than credit card numbers. SSNs accounted for 35 percent, while credit card numbers rang in at 30 percent in breaches. The reason for this shift was attributed to the Equifax breach.

As predicted, with fraudsters’ techniques getting smarter, they are adapting quickly to different types of fraud. The big concern in the financial world around identity theft is the ability to commit synthetic identity fraud.

While the Equifax breach has dominated headlines about fears over credit card fraud and identity theft, there are many less widely-reported incidents that occur on a much more regular basis.

The real impact of financial fraud is its long-lasting effect once a consumer’s personal credentials are compromised. From tracking down where and when the fraud occurred, how much damage was done, and what other records may have been breached, credit card fraud presents plenty of expensive headaches for both cardholders and issuers.

But where do the most credit card and identity theft risks exist? A new report from Reward Expert analyzed data from the the  the FBI’s Internet Crime Complaint Center, FTC’s Consumer Sentinel Report and complaints filed with the Consumer Finance Protection Bureau to determine which states are considered the most ‘at-risk’ for these types of fraud. Here’s what the report concluded.

The roller coaster price fluctuations of bitcoin has kept the cryptocurrency prevalent in the headlines for months. This week, bitcoin made it to the list of credit card fraud concerns, with — as you can probably guess — stolen identities being the root of the problem.

A LA Times article explored the chief concerns around cryptocurrency scams and why banks are getting increasingly skeptical about bitcoin transactions. To start, major banks have reportedly said they will decline transactions that involve customers using credit cards to buy bitcoin. The concern here is that borrows will avoid paying for those bitcoins, particularly if they buy high and bitcoin's price plunges. But the credit risk fears from financial institutions goes far beyond simple purchases of the cryptocurrency itself.

Thanks to more sophisticated fraud detection and prevention technologies, banks are getting better at stopping fraud attempts. A new report from the American Bankers Association revealed that banks were able to stop $17 billion in fraud attempts in 2016.

What that means, according to the 2017 American Bankers Association Deposit Account Fraud Survey Report, is that banks were able to prevent $9 out of $10 attempted deposit account fraud in 2016. In response to the rapidly-growing sophisticated fraud schemes, banks are integrating more technologies in order to enhance their customer protection measures. Typically, this means smarter security software, and newer technology innovations like platforms that rely on machine learning capabilities and data analytics to detect fraud faster and more accurately. 

When discussions about fraud, payments and security arise, you won't get long into a conversation before machine learning and artificial intelligence (AI) come into the mix.

Through the application of high-performance software, machine learning technology has created advanced computing abilities that have a broad-scale reach for community banks that allow them to better compete against the bigger banks. This has created a new reality for issuers looking to enhance their fraud detection tools beyond basic what's readily available in the marketplace today.

As a fraud analytics firm that leverages machine learning technology to better predict and stop the spread of fraud from compromised card details, you could say we’re already pretty bullish about the future of machine learning/artificial intelligence (AI) and its ability to transform the world of fraud detection.

Just ask our team of data scientists if you need more evidence. But you don’t have to take our word for it — there’s plenty of others across the industry echoing the same sentiment. It’s clear machine learning and AI have officially become the trend to watch in 2018.

The year 2017 won’t be the last year that’s remembered for producing a record number of data breaches. There’s a good chance the same story will be repeated for years to come. But what everyone can learn from the growing number of incidents is why greater breach prevention and detection is needed more now than ever.

To wrap up the year, we’ve gathered a list of major data breaches that made headlines in 2017, starting with the incident that will continue to make news long into 2018.

With just over a week left in 2017, the year has already set another record number of data breaches — far surpassing 2016’s figures.

The latest data from the ITRC shows that, as of December 20, there have been a total of 1293 breaches, with a total number of records exposed reaching 174,253,442. To put that into context, in all of 2016 there were 1,093 breaches. That figure was a 40 percent hike over the 780 reported breaches in 2015.

Here at Rippleshot we’re all about the power of data. More specifically, the power of data to help issuers make more strategic decisions when it comes to analyzing risk from compromised cards, managing the spread of fraud and implementing more effective reissuance strategies.

In the final weeks of the holiday spending rush, issuers will be working diligently to analyze consumers spending behavior, track fraud patterns, determine where there are gaps in their fraud management, and decide how it all fits into their 2018 strategic planning. This includes how they’ll educate their cardholders how to better protect themselves, and what types of fraud solutions they’ll invest in to better protect their cardholders — and their own bottom line.

To help issuers wrap up the 2017 holiday season plans — and prepare for 2018 — we’ve gathered our three latest guides on the subject to help you stay updated on the latest trends in fraud management.

While most attention at the moment in Washington, D.C., is on the heavily debated tax bill, there’s another proposed bill that could have major implications for businesses hit by a data breach.

A re-introduced bill, the Data Security and Breach Notification Act, proposes harsher sentences for company executives who fail to notify consumers of a breach. The initial terms of the bill calls for jail time for those who are aware of breaches, yet fail to alert consumers in a timely fashion.

"We need a strong federal law in place to hold companies truly accountable for failing to safeguard data or inform consumers when that information has been stolen by hackers," said Sen. Bill Nelson, D-FL, a sponsor of the bill, wrote in a statement. "Congress can either take action now to pass this long overdue bill or continue to kowtow to special interests who stand in the way of this commonsense proposal. When it comes to doing what's best for consumers, the choice is clear."

The early data on holiday spending — coupled with early holiday fraud projections — gives some insight into what issuers might expect as the year 2017 rounds down.

To start, we’ve learned what most already knew: Consumers love to shop online. This year’s Black Friday/Cyber Monday figures confirmed that fact as consumers shelled out $5.03 billion online on Black Friday and Thanksgiving Day (a 16.8% increase from the year prior). Cyber Monday took that figure to the next level, and consumers set another record-high with $6.6 billion in sales. Overall, Cyber Monday processed 17% more purchases than Black Friday.

More online shopping inevitably means more money is being spent on credit cards, but it also means there’s a greater chance of CNP fraud as fraudsters shift their tactics toward targeting online purchases. This is in line with what industry projections show this holiday season.

The holiday shopping season is underway, which means an uptick in consumer spending. What this also means is an increase in card fraud. Whenever shoppers are spending more, fraudsters are quick to follow — which includes taking part in the holiday shopping season themselves. 

Banks and credit unions are less likely to stop authorization on purchases in order to avoid creating extra friction at checkout, and fraudsters have caught onto this habit. More than ever, consumers need to be savvier and know what red flags to look for this holiday season as they partake in the holiday shopping season. Likewise, card issuers need to be equipped with the knowledge (and tools) necessary to combat holiday card fraud. 

Let’s face it. We’re all bombarded every day with too many reports, too many articles and too many studies to keep up with. At Rippleshot, we work hard to sort through all the noise to bring you relevant news, tips, and resources you can really use to make your operation smoother by equipping your teams with tools to learn how to fight fraud faster and more effectively. Catch these 10 resources.

Most financial institutions have recognized the true value of the technology effectiveness behind machine learning: The ability to discover patterns across millions of data points and hundreds of variables faster and more accurately than human beings. For predicting and stopping the use of compromised card details and the spread of card fraud, this technology is a game-changer.

The real obstacle forfinancial institutions boils down to the concept of implementing such a solution. Without in-house data scientists to create models, the IT resources to get the data from their internal systems or from their processors, and the expertise to insure that the data is clean and correct, implementing processes within an organization can seem overwhelming. But it doesn't have to be so complicated.