Rippleshot Blog

Rippleshot Gives Community Banks and Credit Unions Competitive Edge with AI-Driven Fraud Protection

Posted by Anna Kragie on Nov 5, 2019 8:00:00 AM

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. 

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Topics: Rippleshot News, Machine Learning, Data Analytics

How Credit Unions Can Mitigate Risk With Big Data, Analytics and Machine Learning

Posted by Anna Kragie on Apr 5, 2019 11:47:54 AM

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.

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Topics: Fraud, Machine Learning, Data Analytics

Using Machine Learning To Gain an Edge in The Financial Services Market

Posted by Anna Kragie on Jan 22, 2018 10:41:30 AM

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.

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Topics: Machine Learning

10 Predictions for Machine Learning and AI in 2018

Posted by Anna Kragie on Jan 5, 2018 9:41:19 AM

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.

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Topics: Machine Learning

10 Data-Driven Resources For Issuers

Posted by Anna Kragie on Nov 17, 2017 8:00:00 AM

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.

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Topics: Fraud, Machine Learning

How Banks Can Fight Fraud in a Post-Equifax Breach World

Posted by Anna Lothson on Sep 29, 2017 11:33:17 AM

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).

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Topics: Fraud, Machine Learning, Data Analytics

How Banks and Credit Unions Can Benefit from Machine Learning

Posted by Anna Lothson on Jun 23, 2017 9:50:22 AM

Artificial intelligence has secured its spot in the FinTech ecosystem — making machine learning the chief AI advancement for companies to watch, particularly as it relates to cybersecurity and fraud mitigation efforts for financial institutions.

Need more proof? Just follow the money.

Companies are spending capital hand over fist on researching, developing, and implementing AI and machine learning technology. In 2016, $5 billion in venture capital investments went toward machine learning alone, and corporate investment in AI overall is predicted to triple in 2017. Not keeping up? Now may be the time to invest in machine learning tech. 

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Topics: Machine Learning

Machine Learning VS You

Posted by Sid Khaitan on Nov 30, 2016 11:21:29 AM

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.

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Topics: Cybersecurity, Machine Learning, Data Analytics