The Simility Blog

Taking the Adaptive Fraud Detection Message to MRC Vegas 2019
Jayan Tharayil
March 19, 2019

On March 18th, over 1,600 industry professionals from 30 countries are expected to descend on Las Vegas for the Merchant Risk Council’s (MRC’s) annual MRC Vegas event. If you’re going, we’d love you to stop by our booth (719) or drop into one of our keynotes or demo presentations. The fact that MRC Vegas is so popular shows us there’s much to do to in the fight against global e-commerce fraud.

Events like this are a great place to share and learn. We’re looking forward to understanding your challenges and explaining how Simility can help with machine learning-powered, adaptive fraud prevention.

A Multi-Billion-Dollar Problem

Online retail continues to expand: the latest predictions from eMarketer estimate that global online sales will top $3.4 trillion this year.1 These figures are great milestones for the industry, but they also represent a major draw for online fraudsters. A Juniper Research report from January 2019 claimed global retailers will lose around $130 billion in digital, card not present (CNP) fraud between 2018 and 2023, and the shift to EMV will channel more fraud online.2

This fraud epidemic feeds on breached data, which continues to flood dark web markets and automated credential testing tools. But it’s also evolving to take advantage of cross-border, real-time payment systems and changing shopping habits. One new tactic is omnichannel fraud, where scammers use stolen card details to buy online and then collect a purchase in-store, thereby bypassing automatic shipping address checks.3

While sporadic efforts by law enforcement deters e-commerce fraudsters, they cannot be solely relied upon to quell this activity.4 The burden falls to online retailers, to protect their bottom-line by utilizing the best prevention technologies available to them.

The Power of Machine Learning

Many current approaches to tackling fraud fail because they don’t ingest enough data or adapt sufficiently. The technical capacity to apply all available data helps fraud prevention teams make informed decisions while training machine learning models to detect intricate anomalies. Data often sits in siloes and it can take weeks to test new rules, making systems slow to respond to ever-changing fraud patterns. A lack of built-in intelligence forces too many manual reviews, adding extra friction for the customer and costs for the retailer. Consumers might blame retailers if fraud is committed in their name, but they may also take their business elsewhere if there is too much friction transacting.

This is where platforms like Simility’s can help. We combine static and dynamic data, and apply multiple machine learning models to add predictive power to the decision-making process. This means fraud attempts are blocked automatically in real-time with a high degree of certainty. The platform is designed to minimize the volume of transactions flagged for step-up authentication or manual review. The result is a highly effective, low-friction fraud prevention solution, that combines the power of machine learning with a rules engine to adapt with great agility as patterns change over time.

At MRC Vegas 2019

We’ll be taking this message to MRC Vegas on March 18-21, so be sure to drop in and say hi. Our experts will be on hand at booth (#719) to explain how we can help to transform your business through cutting-edge fraud prevention.

Also catch Simility at the following speaking engagements:

  • March 19, 3:15pm: Ritesh Arora, Director of Data Science, will be taking part in a panel discussion on machine learning
  • March 21, 1:15pm: Vanita Pandey, Senior Director of Product Marketing and Strategy, and CTO/Co-founder Kedar Samant will present a keynote on Evolution and Innovation in APAC
  • Simility will also be showcasing its solutions at the Demo Theater.

We’re looking forward to seeing you at the show. Pre-book a meeting with us or visit our booth #719 and collect some amazing Simility swag.

1. Statista,
2. Juniper Research,
3. Simility,
4. Europol,