Defeat Fraud with a Comprehensive AI-powered Solution
A three-part series focusing on fraud modeling: pre-modeling, modeling, and post-modeling.
Bringing DataVisor to the Masses
Why I’m looking forward to representing a truly world-changing AI-powered fraud solution.
Bot attacks, and one airline’s battle to defeat them
As malicious bot attacks become more sophisticated, the ticketing industry is feeling the pressure. One airline is fighting back.
Powering Our Success: Remarkable Women at DataVisor
We spoke with six women at DataVisor about their careers, their accomplishments, and the opportunities and challenges of life in Silicon Valley.
Why I Joined DataVisor
DataVisor’s singular combination of ethical mission, visionary talent, and cutting-edge product, made this writer’s next career choice an easy one.
DataVisor Introduces DCube
DCube is the complete AI-powered fraud management solution that puts control in the hands of fraud and data science teams to proactively defeat emerging fraud.
The Worrisome Rise of Credential Stuffing
Bot-powered credential stuffing attacks are coordinated, automated, and massive in scale. To prevent ATO of this type, advanced fraud management is required.
Synthetic Identity Theft – When Credit Risk is Not Credit Risk
Synthetic identity theft - when a fraudster creates a composite identity from a mix of real and fake information and applies for loans with the identity - is a growing problem for financial institutions.
Are Mobile Devices the Leading Target for Fraudsters?
Fraudsters are constantly finding ways to commit fraud through the mobile channel. This post highlights the techniques and methods they use.
Digital Fraud Trends for 2018 and Outlook for 2019
DataVisor's CEO Yinglian discusses the biggest trends in digital fraud from 2018 and makes predictions for what 2019 will bring.
What Fraudsters Are Doing with Breached Data
There were 944 data breaches in the first half of 2018 alone, compromising 3.3B +data records. Learn how fraudsters use this breached data to monetize the breaches and inflict damage.
3 Keys to Fraud Detection for the Modern Wave of Sophisticated Fraud
DataVisor research insights on the rising wave of sophisticated fraud, proactive ways for fraud prevention and role of unsupervised machine learning
Deconstructing Recent Data Breaches
This blog post highlights several recent data breaches explaining how each breach happened and the number of consumers impacted.
5 Essential Ways to Protect Marketplaces from Holiday Fraud
The holiday season provides fraudsters a perfect ground to attack. DataVisor’s Unsupervised Machine Learning can help buyers & sellers in marketplace.
Senate Bill 2155 Aims to Stop Synthetic Identity Fraud
This post provides a few details about synthetic identity fraud and S.B. 2155, including what businesses should expect when it comes to the rollout of the law.
Combating the Unknown in Application Fraud with AI
Application fraud is sophisticated and legacy systems are unable to combat it. DataVisor’s Unsupervised Machine Learning identifies hidden attack patterns.
The Commoditization of Supervised Machine Learning
With Supervised ML becoming increasingly commoditized, businesses are often left with various components rather than a solution that provides real value.
The Battle of Uncovering Fake Accounts
The first step for fraudsters to commit an intended fraud on a given platform begins with fake account creation. Learn how AI and Machine Learning can help reduce fake account creation.
Dealing with the Complexity of Fraud Attacks in Mobile Application Fraud
The fraud landscape within the mobile user acquisition space is very complex with many sophisticated attack techniques involved. In this blog post, we will cover the tools and techniques used by fraudsters and why it's difficult to detect them.
Money20/20 2018: Top Three Takeaways for Fraud and Risk Teams
Last week, DataVisor attended Money20/20 Las Vegas, the latest financial and payment technology conference of the year. Here are the top three takeaways from the event for fraud and risk teams.
Uncovering Hidden Patterns to Improve AML Detection
Evolving money laundering patterns are leading to huge fines and mounting pressure on FIs to become more vigilant. Learn how unsupervised machine learning and its inherent merits can help FIs to uncover hidden money laundering patterns and improve AML detection.
Top Five Insights Needed for Unknown Fraud Analytics
Are your fraud analytics tools ready for unknown fraud detection? Here are the top five considerations and insights needed for a dashboard to provide actionable insights for unknown fraud.
Model Validation: 3 Core Elements for SR 11-7 Compliance
An SR 11-7 compliant validation framework includes 3 core elements: An evaluation of conceptual soundness, ongoing monitoring, and outcomes analysis.
How Fraudsters Are Derailing User Acquisition Ad Campaigns – Part 1
This blog post is part one of a two-part series that details the UA fraud problems in the mobile app industry. The series highlights the impact of the fraud problem, the tools and techniques fraudsters use and why UA fraud is getting harder to detect.
Can Your Known Fraud Tools Fight Unknown Fraud?
The digital economy is witnessing sophisticated and previously unknown fraud attacks like never before and existing fraud solutions are struggling to keep up.
Emerging ATO Tactic: Call Center Scams – Part 2
Part 2 of the blog series highlights what financial institutions are currently doing to prevent call center fraud and the downstream problem of ATO fraud, and what’s missing with those solutions.
Embracing AI & Machine Learning for AML
Today's AML & Compliance leaders face dual challenges of increasingly sophisticated digital financial crimes and the threat of growing fines from regulators. Learn how AI and Machine Learning can help FIs detect more crime and better triage alerts.
Diving into Q2 2018 DataVisor Fraud Index Report
Learn about the latest trends and insights in the world of online fraud from our analysis of over 4 billion accounts from some of the largest internet properties and financial services in the world.
Emerging ATO Tactic: Call Center Scams – Part 1
Advances in defensive measures against ATO have led to a change in tactics for fraudsters- many fraudsters are now setting their sights on financial institution call centers to commit ATO fraud.
Best Practices for Fraud Managers: Selecting a Fraud Prevention Solution – Part III
This is the final part of a three-part blog post series highlighting some of the key things to look for when it comes to choosing a third-party fraud prevention solution. In this post, we go over topics such as adaptability, scalability, ease of integration, and flexibility in deployment options.
Best Practices for Fraud Managers: Selecting a Fraud Prevention Solution – Part II
This is part two of a three-part blog post series highlighting some of the key things to look for when it comes to choosing a third-party fraud prevention solution. In this post, we go over topics such as explainability, engaging visualizations, data privacy processes, and etc.
Best Practices for Fraud Managers: Selecting a Fraud Prevention Solution – Part I
This is part one of a three-part blog post series highlighting some of the key things to look for when it comes to choosing a third-party fraud prevention solution. In this post, we go over topics such as multi-layer protection, target use cases, global reach and data, etc.
How DataVisor Optimizes its AWS Stack to Protect 4B+ Online Accounts from Fraud
DataVisor's VP of Engineering David Ting discusses how DataVisor optimizes its AWS stack with spot fleets, dynamic instance launches, & RT cost tracking, all while protecting over 4B users from fraud at the AWS Summit Anaheim 2018.
How IT Teams are Leading Their Organization’s Fight Against Fraud
Preventing fraud and protecting users is integral to IT strategy and core to maintaining a company’s competitive advantage. Learn how IT teams can be proactive on the lookout for fraud and able to react to it at the speed it happens.
A Double-Edged Sword: The Convenience of Online Lending for Customers and Fraudsters
Online digital lenders have proliferated in the last few years, and traditional lenders have also rebalanced their focus and have increased their digital efforts across all products trying to catch up with their nimbler rivals. As a result, the potential target for fraudsters to attack has become significantly larger and more lucrative and they haven’t held back their efforts to inflict maximum financial damage.
Emerging Fraud in Marketplaces: How Product Listing Fraud Is Gaining Traction
Fraudsters are constantly coming up with new and innovative ways to commit fraud. Today we are taking a look at product listing fraud, a relatively new type of fraud that is a rapidly growing problem for online marketplaces.
How Spammers Conduct Mass Spam URL Attacks
A successful spam campaign is one that obtains maximum return-on-investment (ROI) to the spammer. This means that a spam campaign must reach as many end users as possible, must be robust in the face of blacklisting efforts, and must be scalable. This blog post describes some of the recent techniques employed by spammers to distribute malicious URLs on social media platforms as observed by DataVisor.
Detecting New and Evolving Fraud Patterns in Digital Commerce
As attacks grow in scale and velocity, businesses are forced to evolve their fraud detection methods from manual detection involving blacklists and rule engines to machine learning algorithms that can detect known and emerging types of fraud. This article highlights why existing fraud detection methods have limitations and more importantly a few reasons why unsupervised machine learning is gaining traction.
Maximizing User Acquisition ROI with AI Technology
In recent years, many mobile applications, including mobile games, have expanded to the global market. However, as they expand, fake traffic is becoming a growing problem that has long plagued many game developers. It directly results in the waste of marketing resources, making it impossible for developers to have real control over the ROIs of acquiring new users.
Managing Thousands of Spark Workers in the Cloud: DataVisor Presents at SAIS 2018
DataVisor's Yuhao Zheng and Boduo Li share advanced techniques for managing thousands of spark workers to analyze billions of events at a time, including clustering workers and automated, optimized management of DataVisor's spark infrastructure.
Fighting Fraudsters Among Billions of Users: DataVisor Presents at SAIS 2018
DataVisor's Ting-Fang Yen and Arthur Meng present a novel deep learning technique for scalable online fraud detection among billions of users.
Technical Blog | Are Labels Useful to Unsupervised Algorithms?
Does the fact that UML doesn’t require labels mean that there is no benefit at all to labels? If label data exists already, how can it be used to improve UML detection results? In this article we discuss how labels can be effectively used in UML detection, even if they are not required.
Unsupervised Machine Learning: A 5-Minute Beginner’s Guide
Unsupervised Machine Learning (UML) is a topic that we get a lot of questions about here at DataVisor, because UML is at the core of our detection platform. In this 5-minute primer on UML, we start by defining the overarching field of Artificial Intelligence, then we drill down to the sub-field of Machine Learning, and lastly we discuss the various machine learning techniques, including UML, and when each ML technique is most effective.
A Few Key Differences Between Supervised and Unsupervised Machine Learning
Introduction There are many technical articles that describe supervised and…
Thank you, Chuck Thacker.
Chuck Thacker was part of the foundation of everything we know in computing and a major influence on our founders and our company. Thank you, Chuck Thacker.
Infographic: The Online Fraudster’s Tool Shed
The DataVisor Online Fraud Report took a look at our base of more than one billion users across 172+ countries in the world. Using this massive amount of data, we were able to identify some of the favorite tools and attack techniques that online criminals from around the globe favor when doing their dirty work.
Guest Post: AML Data Quality – The Challenge of Fitting a Square Peg into a Round Hole
As mentioned in my previous articles, traditional rule-based transaction monitoring…
Introducing the DataVisor Online Fraud Report
As DataVisor Co-Founder and CEO Yinglian Xie says in her…
Guest Post: The Other Elephant in the Room: Defeating False Negatives in AML Systems
False positives have a terrible reputation among anti-money laundering (AML)…
Guest Post: End the False Positive Alerts Plague in Anti-Money Laundering (AML) Systems
Keith Furst is the Founder of Data Derivatives, and has…
Guest Post: Look at the Bricks Again – Inside the Mind of a Fraudster
With the rapid popularization and development of the Internet, a cesspool of online possibility has opened for nefarious individuals with an eye for rampant security gaps.
Twitter Bots: These are the Droids You’re Looking For
Wondering if your company has any crime rings sleeping among your users? Most will acknowledge that there are likely some accounts lurking here or there, but may not realize that it’s a big problem.
Datavisor’s Fraud Trends and Predictions for 2017 – Thoughts on What’s Ahead
Predictions season is upon us once again and after some…
Guest Post: Lock It Down and Smarten Up – Best Practices for Online Security
This is a guest post from Zack Pumerantz, fraud prevention manager…
Don’t Be Taken to the Cleaners – Money Laundering Techniques
Money laundering is big business. Criminals use money laundering techniques…
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