Rippleshot Blog

Originally Posted Feb 10, 2022 on The Paypers

João Moura, the CEO of Fraudio, discusses how AI models can outsmart merchant initiated fraud and help PSPs and acquirers onboard more merchants in order to grow faster, smarter, and safer

Originally Posted Jan 21, 2022 on Dark Reading

The acceleration of the digital transformation resulted in a surge of online transactions, greater adoption of digital payments, and increased fraud.

Originally Posted Jan 11, 2022 by Northwest Credit Union Association

When it comes to preventing card fraud, issuers need all the help they can get. Fortunately, credit unions have access to a variety of tools, resources, and expertise in their fight against fraudsters’ ever-changing tactics.

Card not present (CNP) fraud is the dominant type of payment fraud that Strategic Link partner CO-OP Financial Services is seeing among its credit unions’ portfolios, comprising over 80% of fraud incidents across both debit and credit.

One key contributor to this rise has been the increasingly bold use of BIN attacks, one of the most common types of CNP fraud.

Originally Posted Oct 4, 2021 by Polly Jean Harrison on The Fintech Times

 n.exchange, a cryptocurrency exchange specialising in fiat on- and off-ramp to make crypto investment user-friendly, unveils its crypto purchase credit card fraud attempt figures for 2019-2021. Its campaign to combat credit card fraud in cryptocurrency highlights a significant rise in fraudulent purchase attempts by cybercriminals using stolen card details, most of which was perpetrated from countries in the Western hemisphere.

Originally Posted Jan 24, 2022 on PYMNTS.com

Originally Posted Jan 8, 2022 by Gordon Kelly on Forbes

Last year saw the biggest hack in iPhone history, complete with individual horror stories from affected users. Now a haunting new discovery could make all iPhone attacks a lot worse.

Originally Posted Jan 21, 2022 by Chuck Brooks

The past two years has seen a rapid shift of work to remote and hybrid offices. The statistics show that hackers welcomed that shift and took advantage of the vulnerabilities and gaps in security by businesses.

Originally Posted Dec 11, 2020 by Alex Rolfe

Nearly half (40%) of merchants are reporting a rise in friendly fraud over the past 12 months, but the majority are struggling to challenge Google Pay and Apple Pay chargebacks successfully, research by Ravelin finds.

Originally Posted Jan 17, 2022 by PYMNTS.com

When you think of a credit union (CU), it’s easy to picture a place that values member relations and face-to-face banking.

While this business model has typically worked to the advantage of CUs, it can also leave some of these institutions ill-prepared for the COVID-fueled shift to digital-first banking — and the risk of online fraud that come with it.

Originally Posted Jan 2022 by GN Feature Story

Banking and financial institutions lose billions of dollars because of fraud. Machine learning can help detect and prevent fraud. 

Machine learning algorithms can reveal fraud patterns much faster and more accurately than humans or traditional rule-based systems. Read this article to understand how exactly banks can benefit from ML-powered solutions in fraud detection.

Each year, banking and financial institutions from all over the world lose many billions of dollars because of fraud. Machine learning seems to be the most efficient technology for detecting and preventing fraud in this rapidly evolving sphere. From this article, you’ll understand how exactly banking and financial institutions can benefit from integrating ML algorithms. Plus, you’ll learn about the shortcomings of traditional fraud detection techniques.

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.