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.
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.
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.
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.
Rippleshot is excited to announce Rules Assist™, an AI-driven decision rules analytics solution to empower community banks and credit unions in the fight against emerging fraud trends.
E-commerce fraud now accounts for roughly 75% of all card fraud, causing financial institutions to race to keep up with fraudsters. In response, top Fortune 500 financial institutions have embedded artificial intelligence and machine learning into their core business models. The four biggest banks in the U.S. budgeted a collective $38.4 billion for innovation and technology in 2019 alone.
Faced with more limited resources, community banks and credit unions often lack the technological edge to keep pace with innovations that greatly impact customer experience. Rippleshot Rules Assist was developed to address technology gaps smaller financial institutions face in their back office to efficiently protect their customers. Financial institutions will be able to cost effectively leverage AI and Machine Learning within their existing infrastructure without adding IT resources or staff.
In an era of rising card fraud and data breaches, credit union leaders are constantly analyzing how they are protecting themselves, and their members. One of the biggest problems today? Waiting for network alerts can be costly in terms of fraud loss and customer experience.
That was one perspective Rippleshot’s Customer Success Manager Jesse Sherwood shared in a webinar she recently participated in hosted by CUNA Mutual Group titled “Managing Risk Through Big Data, Analytics & Machine Learning.” Managing that risk, Sherwood said, starts with determining how data can be used to identify and act on fraud sooner.
“When we are thinking about data, we have to start with the problem. What problem are we trying to solve?,” Sherwood said during the webinar. “Data breaches are becoming more and more common and at very large scale. What this means is credit unions and members are being impacted. How do we protect them?”
The answers to those questions start by determining what tools can help credit unions boost fraud prevention performance.
Developing trust as a fraud analytics platform doesn’t happen overnight. Here at Rippleshot, we’ve been working for years to make our fraud platform more than just about the data science. We’re all about the people, too.
In a recent interview with Bizcast, our co-founder Canh Tran shared his perspective on what it takes to grow a startup in a rapidly-changing industry — while still delivering customer success through our people-centric model.
Topics: Data Analytics
The Equifax breach that continues to make headlines is a game-changer for the financial services space. The biggest fear, of course, remains the unknown cost impact for banks and credit unions.
Inevetiably, in a breach affecting roughly half the U.S. population, the scope of this incident will be long-lasting. The end results won’t be known for some time since the real threat ahead lies in fraudsters’ ability to create false identities (AKA: synthetic fraud).
To help combat the fallout from this breach, we've gathered four tips that banks and credit unions should keep in mind as they devise their strategies for keeping up with the spread of fraud (and fraudulent accounts).
Have you ever looked at your computer or phone in awe, and considered the possibility that it may be smarter than you? Although the philosophical debate surrounding the nature of intelligence has waged on for decades, the advent of machine learning has caused it to suddenly resurface. After all, when a computer can comb through years of company data and solve a complex problem within seconds, it is hard to not give heed to the argument that technology is smarter. Regardless of whether or not intelligence can be measured, the final answer is that neither is smarter, and both must work effectively together in order to find solutions to tomorrow's problems. Follow the Rippleshot Team as we discuss the origins of machine learning, its implications for the future, and how you can leverage its power to benefit your institution.