Rippleshot Blog

Dickey's Barbecue Pit, a restaurant franchise with 156 locations across 30 states, was hit by a malware-based, point-of-sale data breach. The details of the breach surfaced after Gemini Advisory, a cybersecurity firm, found the stolen cards on a Joker’s Stash, a hacker’s forum for stolen payment data. The data was traced back to the compromised point of purchase (CPP) — Dickey’s Barbecue Pit.

It's believed payment systems were compromised by card-stealing malware, with the highest exposure believed to be in California and Arizona. It’s believed the transactions were made with magstripe cards, and the breach could have occurred on a single central processor, according to Gemini. Reports indicate that since about mid-2019, credit card data from roughly 3 million payment cards were stolen. It's believed the +3 million credit cards stem from 35 states, spanning a time frame of over a year.

With fraudsters exploiting new technologies and leveraging sophisticated tactics to compromise card credentials and account data, fraud analysts on the front line, like yourself, know they must have tools to move faster and smarter.

Tools that leverage AI models fit the bill for fraud managers looking to streamline fraud detection and analysis efforts. AI-powered models collate vast piles of data into actionable strategies for stopping fraud. All without you wasting hours of your time on analysis.

Artificial Intelligence and machine learning have become buzzwords in the fraud detection space, but not not all fraud leaders understand the vast benefits of this technology as it relates to optimizing your fraud mitigation efforts. Finding the right structure to integrate all the data that fraud managers must analyze into one system can be challenging. AI models set the right foundation to build from.

Fraud managers are busy. We know it's hard to find a one-stop resource shop for determining how to choose the right fraud tools that align with your fraud strategies and organization goals. The Rippleshot team is here to be your guide to approach this complex space. 

Recently, we've put together a few short pieces on how to navigate the fraud solution ecosystem, including some actionable tips on what you should consider when selecting the right path for your organization. Below are three resources we've put together to help your team get on the right path to lowering fraud costs and hitting your goals.

"Success isn’t just measured by how many customers you can bring on, but also by how many you can retain. One of the leading causes for customers to close their cards is the feeling that their issuer isn’t doing enough to secure their accounts." 

Fraud and risk managers and analysts at community banks and credit unions face similar hurdles when selecting a fraud analytics tool that meets their fraud management goals.

They are equipped with droves of transaction, merchant and cardholder data, but don’t quite have the time or resources to know if their fraud detection efforts are effective. Nor do they always know what to do with all that data. They also don’t always have access to enough data, or the right tools. The end result is a gap in fraud detection and prevention efforts that makes it difficult to balance fraud costs and expense ratios. 

The pain points among fraud professionals are also common. They are burdened with evolving fraud threats, compliance challenges, and an inability to know what fraud risks are coming and what high-dollar events might hit next.

Luckily for financial institution leaders, the evolution of cloud technology, enhanced data security and the application of AI and machine learning technology has paved the way for fraud analytics tools to address these pain points. 

To help guide your decision making process as you ramp up your fraud mitigation efforts, we’ve broken down 5 questions worth asking yourself when determining how to choose a fraud analytics tool. 

Fraud analysts and managers are the top line of defense against evolving fraud threats, causing them to spend countless hours fine tuning fraud rule writing and conducting analysis on these efforts.

They’re tasked with mitigating incidents, reducing fraud losses and false declines — all without disrupting cardholders. They wear a lot of hats, and often lack access to enough data or tools to know if their efforts are actually effective. 

At smaller financial institutions, fraud analysts and managers often have a small team, or sometimes are a “team” of one. The fraud rule writing processes they manage are tedious, time-consuming and hard to judge if they are actually effective. Frauds can be easily overlooked without the right tools in place.  We're here to share the common pain points we hear about fraud rule writing and how you can assess your fraud mitigation efforts. 

We hear many common pain points from fraud analysts and managers.

“I don’t know what fraud risks are coming my way…I worry about the high-dollar events I can’t see coming…fraud analysis takes too long…I miss too much fraud until after it happens…I can’t do my job without an entire team of fraud analysts.”

The list goes on and on — and these pain points escalate when big fraud events hit. 

With the right implementation of AI, machine learning and big data to know where, when and how the biggest merchant risks are impacting your cardholders, these pain points can be proactively alleviated. Instead of relying on reactive strategies that cause your fraud team to respond to incidents as they are occurring, or after it’s too late to stop their spread, the application of the right data and technology can help your team get early warnings about where fraud is occurring before it hits your institution.

New data from a report published by Fortune Business Insights underscores the need for financial institution leaders and fraud managers to align their digital transformation and fraud detection and prevention goals. This report specifically highlights how today's fraud trends have spurred faster adoption of technologies that rely on big data and predictive analytics to detect fraud.

An increased demand for solutions that rely on predictive analytics to detect fraud that's occurring and prevent potential fraud through automated pattern analysis, has paved the way for financial institutions to proactively protect their customers. The application of AI and machine learning has given fraud managers the necessary tools to prevent payment fraud before it occurs, and reduce fraud losses and associated costs when they do occur.

The rise in online fraud on a global scale has caused Fortune Business Insights to project the global fraud detection and prevention market to grow to $110.04 billion by 2026, a 25% growth in a 6-year period. The report specifically notes why machine learning and AI-based fraud detection in the banking sector is fueling the fraud detection and prevention market growth. The report underpins one key obstacle that's holding many financial institutions back: "Limited Data Visibility Often Produces False Positives Outcomes." 

The Financial Crimes Enforcement Network (FinCEN) issued another COVID-19 related alert for financial institutions about cyber-enabled crime and payment fraud schemes that continue to rise during the pandemic. The latest alert, issued July 30, focuses on the exploitation of remote platforms, email compromise campaigns and phishing, malware and extortion campaigns. 

The advisory notes the following information should be shared across financial institutions with the following people: CEOs, COOs, chief risk officers, chief compliance/BSA officers, BSA/AML analysts/investigators, IT, cybersecurity units, fraud prevention units and legal departments. The latest alert follows an advisory posted on July 16 about the uptick in business email compromise schemes. These trends align with the uptick in overall fraud reported to financial institutions as noted in the FTC's data on COVID-19 related payment fraud reports. We break down the data in this report.

The push for greater digital transformation across the financial services market isn't just changing the face of the front-end customer experience, it's rapidly-impacting how banks invest in back-end technologies that detect and mitigate emerging fraud threats.

The ABA Banking Journal's Risk and Compliance team recently interviewed Rippleshot's CEO Canh Tran about the greatest fraud threats impacting financial institutions in 2020, and how fraud teams can  proactively mitigate these risks. 

“Today, the real trend for both fraudsters and bank fraud managers is the use of technology to be more effective and efficient,” Tran said, “Digital transformation, data aggregation, machine learning, predictive algorithms, and cloud computing to be more effective—and unfortunately the fraudsters are more advanced.”

Bottom line? As banks get smarter, so do fraudsters.

Two findings from a recent industry study highlight the benefits AI and machine learning and what role these technologies play in the digital transformation of community banks and credit unions.

1. "New technologies will drive banking transformation over the next 5 years."

2. "Artificial intelligence will separate the winners from the losers in banking."

This report underpins the impact COVID-19 is having on financial institution cloud technology investments and how advancements in machine learning-driven software will help them weather the storm. These insights, gathered from a poll of poll of top banking executives, points toward the need for greater digitization across the financial services ecosystem. Of course, this will also open up the floodgates for another problem: New channels for fraudsters to exploit and monetize. We've broken down how financial institution leaders can assess their fraud detection strategies, where machine learning plays a pivotal role in mitigating risk and why this matters for 2020's fraud trends. 

There is no one-size-fits-all approach to combating the rise of synthetic identity fraud — the fastest-growing type of financial crime, according to McKinsey's research. Instead, financial institutions must take a collaborative approach across the fraud detection industry and leverage comprehensive insights and advanced technology to combat the ever-evolving problem. These recommendations were highlighted in the Federal Reserve's July 2020 report, Mitigating Synthetic Identity Fraud in the U.S. Payment System.

The Fed's report pulls from expert analysis that details actionable tips for how financial institutions — particularly smaller ones — should collaborate and partner with organizations that can provide access to more data and fraud detection tools to FIs that otherwise wouldn't have these resources. 

"Consortium data is better than organization-level data in detecting trends. Information sharing is particularly important for smaller financial institutions, which generate less data and may have fewer technological and fraud-fighting resources than larger companies," the report noted.

The pandemic has underscored the need for businesses to be prepared for unforeseen threats. For banks and credit unions, this means having the insights and the right tools to proactively combat emerging card fraud threats. Rippleshot's CTO Yueyu Fu recently shared his perspective with the team at UI Ventures about how businesses are leveraging AI-driven technology to thwart fraud threats during these uncertain times. 

“COVID-19 has drastically changed how businesses are operating, including how they manage fraud risks. We’ve made it a priority to provide data-driven research on how this crisis is impacting spending habits, fraud patterns and the overall economic outlook,” Fu told the team at IU Ventures in a recent interview. “This information is critical for financial institutions to know what they can do to protect themselves and their customers during this difficult time.”

COVID-19 has forced financial institution leaders to pivot their physical footprint plans and drastically adapt how they think and invest in banking technologies.

A new study indicates COVID-19 has made banking executives accelerate their plans for investing in new tech such as cloud-based tools and AI. The desire is growing for new tools that can help organizations be more efficient, cut costs and proactively thwart off emerging financial threats to their organization and their cardholders. Another new report shares deep insight into how the pandemic is driving the fraud detection and prevention market. 

Mobile banking app usage has surged as access to physical banks has been limited during the COVID-19 pandemic, which has spurred the FBI to issued a warning about the potential for hackers to exploit mobile channels.

“The FBI advises the public to be cautious when downloading apps on smartphones and tablets, as some could be concealing malicious intent,” the nation’s law enforcement agency wrote in the alert. "The FBI expects cyber actors to attempt to exploit new mobile banking customers using a variety of techniques, including app-based banking trojans and fake banking apps."