Rippleshot Blog

How Financial Institutions Use Machine Learning to Prevent Fraud

Posted by Rippleshot on Jan 12, 2022 4:16:48 PM

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.

Read More

Topics: Fraud, Machine Learning, Data Analytics

How AI Models Streamline Fraud Detection and Analysis

Posted by Anna Kragie on Oct 6, 2020 10:00:00 AM

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.

Read More

Topics: Fraud, Machine Learning

Why Fraud Analysts and Managers Need Machine Learning and AI Tools

Posted by Anna Kragie on Aug 14, 2020 10:00:00 AM

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.

Read More

Topics: Fraud, Machine Learning

Machine Learning Benefits for Community Banks and Credit Unions

Posted by Anna Kragie on Jul 17, 2020 12:18:31 PM

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. 

Read More

Topics: Fraud, Machine Learning

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. 

Read More

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.

Read More

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.

Read More

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.

Read More

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.

Read More

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).

Read More

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. 

Read More

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.

Read More

Topics: Cybersecurity, Machine Learning, Data Analytics