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AML Data Quality: The Challenge of Fitting a Square Peg into a Round Hole

By | April 17th, 2017|Technical Posts|

As mentioned in my previous articles, traditional rule-based transaction monitoring systems (TMS) have architectural limitations which make them prone to false positives and false negatives: Naive rules create a plague of false positives that are expensive for investigators to sift through Sophisticated money launderers know how to circumvent rule-based systems leading to false negatives and [...]

Introducing the DataVisor Automated Rules Engine

By | April 2nd, 2017|Company News, Product Announcement|

Today, we’re excited to publicly announce the DataVisor Automated Rules Engine, a rules engine that maintains itself. By eliminating the need for human effort to add, tune, and delete rules, it’s the most sophisticated rules engine on the planet. At a glance, the Automated Rules Engine: adds, deletes, and tunes rules backtests all rules against [...]

Introducing the DataVisor Online Fraud Report

By | March 15th, 2017|Company News, Technical Posts, Threat Blogs|

As DataVisor Co-Founder and CEO Yinglian Xie says in her foreword to the Inaugural DataVisor Online Fraud Report, “Data is power.” We have no shortage of data here at DataVisor and we have taken the opportunity to unlock it and analyze the results. Through our Global Telemetry Network of more than one billion users across 172+ [...]

Guest Post: The Other Elephant in the Room: Defeating False Negatives in AML Systems

By | March 5th, 2017|Technical Posts|

False positives have a terrible reputation among anti-money laundering (AML) circles. As mentioned in my previous article on ending the false positive alerts plague, approximately 90-95 percent of alerts generated by Transaction Monitoring Systems (TMS) are false positives. So, why don’t we tighten our rule thresholds to let fewer alerts through? Image Source [...]

Guest Post: End the False Positive Alerts Plague in Anti-Money Laundering (AML) Systems

By | February 12th, 2017|Technical Posts|

Keith Furst is the Founder of Data Derivatives, and has years of experience within a variety of financial institutions including Tier One wholesale banks, investment banks, foreign bank branches, commercial banks, retail banks, broker-dealers, prepaid card providers and merchant acquirers with a focus on implementing, fine tuning and validating financial crime systems. His forte relates [...]

Guest Post: Look at the Bricks Again – Inside the Mind of a Fraudster

By | February 6th, 2017|Quick Takes|

This is a guest post from Zack Pumerantz, fraud prevention manager at FanDuel. At FanDuel, Zack is responsible for proactive investigation, chargebacks, reporting and training his quickly growing team of cyber crime specialists.  Zack's team brings to the table a ruthless drive to catch criminals, a focused approach and team chemistry reminiscent of the '96 Bulls. [...]

Datavisor Protects One Billion Accounts

By | January 26th, 2017|Company News|

One billion.  It’s a number that has been all of the headlines recently, with Yahoo disclosing a breach of more than one billion user accounts in August 2013. It’s hard to fathom a breach that large, but as we noted back in August of 2015, we are entering an era of billions of users and [...]

Twitter Bots: These are the Droids You’re Looking For

By | January 23rd, 2017|Quick Takes, Technical Posts|

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. This attitude is held by a lot of companies, large and small, and many think they have it under control. Think [...]

Datavisor’s Fraud Trends and Predictions for 2017 – Thoughts on What’s Ahead

By | November 30th, 2016|Quick Takes|

Predictions season is upon us once again and after some successful speculation for 2016, we decided to take another Nostradamus-like crack at it again with what we predict will happen in the world of online fraud trends in 2017. Fraud and security are interesting areas in which to try to make predictions since the enemy [...]

Money Laundering and Meeting Up – A Recap

By | October 28th, 2016|Meetups|

Money laundering. It sounds clean, but it’s a dirty business. According to PWC, “global money laundering transactions are estimated at 2 to 5% of global GDP, or roughly U.S. $1-2 trillion annually.” As attack techniques become more sophisticated, the landscape for money laundering is changing quickly. This means that the teams who detect and fight [...]

Guest Post: Lock It Down and Smarten Up – Best Practices for Online Security

By | October 17th, 2016|Quick Takes, Technical Posts|

This is a guest post from Zack Pumerantz, fraud prevention manager at FanDuel. At FanDuel, Zack is responsible for proactive investigation, chargebacks, reporting and training his quickly growing team of cyber crime specialists.  Zack's team brings to the table a ruthless drive to catch criminals, a focused approach and team chemistry reminiscent of the '96 Bulls. [...]

Don’t Be Taken to the Cleaners – Money Laundering Techniques

By | September 22nd, 2016|Meetups, Quick Takes|

Money laundering is big business. Criminals use money laundering techniques to cover up funds acquired through illegal activity and make it appear as if they were generated through legitimate and legal means. The fraudster aims to conceal that the funds exist, how he or she acquired them and where they are stored. Whether it’s Walter [...]

Bad Account Opening: Fake Accounts and Real Big Trouble

By | September 15th, 2016|Quick Takes, Technical Posts|

Wells, wells, wells, what do we have here? Last week the news broke that Wells Fargo had “been hit with $185 million in civil penalties for secretly opening millions of unauthorized deposit and credit card accounts that harmed customers,” and the backlash caused by the account opening has been harsh. While the ethical issues with the [...]

A Guide To Hiring Your Fraud Team Part 2: Automate, Motivate and Innovate

By | September 7th, 2016|Meetups, Technical Posts|

Matching wits with a nefarious user. Solving a puzzle where you have to find missing clues. Determining what steps they’ll take next. Getting an adrenaline rush when hot on the trail of a bad guy. Protecting good customers and keeping them safe. These all describe a typical day in the life of a fraud analyst. [...]

What We Learned: Fraud Detection Tools Webinar

By | August 29th, 2016|Meetups, Quick Takes|

Our first webinar is in the books! In our Fraud Detection Tools Webinar, DataVisor co-founder and CTO Fang Yu and I broke down four major tools for fraud detection: Reputation Lists, Rules Engines, Supervised Machine Learning, and Unsupervised Analytics. Based on my blog post, “Unsupervised Analytics: Moving Beyond Rules Engines and Learning Models,” we looked at [...]

Where to Begin – A Guide to Hiring Fraud Analysts and Building Your Team

By | August 17th, 2016|Meetups, Quick Takes, Technical Posts|

In my last post, I talked about technology and, in particular, components in a fraud-detection architecture. But when it comes to building a fraud strategy, it’s not just about technology. Perhaps equally, if not more, important are the people who analyze and review cases to catch fraudsters. This post is part one of a two [...]

Spend Money to Make Money – Virtual Currency Arbitrage

By | August 11th, 2016|Quick Takes|

There’s an article in the Wall Street Journal today that takes on how virtual currency in internationally popular games, such as the near ubiquitous Pokémon Go, can cause interesting financial dilemmas for their creators. The article, “Pokémon Go Illustrates a Currency Problem,” highlights how Nintendo, the company behind Pokémon Go, could face making less money [...]

How Risky is Your UA Business? Introducing the User Acquisition Fraud Calculator

By | August 8th, 2016|Company News, Meetups, Quick Takes|

One of the things we heard repeatedly during our most recent meetup on User Acquisition Fraud was frustration at not knowing where a company stands in terms of fraudulent users. It was clear that people want to fix or address any fraud issues, but aren’t quite sure if they have any fraud issues at all. How big [...]

Unsupervised Analytics: Moving Beyond Rules Engines and Learning Models

By | August 1st, 2016|Technical Posts|

Rules engines, machine learning models, ID verification, or reputation lookups (e.g. email, IP blacklists and whitelists) and unsupervised analytics? I've often been asked which one to use and should you only go with one over the others. There is a place for each to provide value and you should anticipate incorporating some combination of these [...]

If You Pay For Users, You Should Pay Attention to UA Fraud

By | July 26th, 2016|Meetups|

As we heard in our last meetup, the priority for any online marketplace or community is protecting its users. Significant resources are spent on time, technology and policies to make sure that users are safe, happy and secure. Since users are also the baseline by which many of these marketplaces, games and communities are judged and [...]