Feature Article: Maximizing Machine Learning Model Performance and Shelf-Life

As fraud professionals, it’s natural to focus on preventing fraud losses, but this often comes at the detriment of sales conversion. The nature of model-based risk management platforms and machine learning model training has this bias as well, mainly as a result of the fact that it is much easier to recognize missed fraud than it is to recognize sales insults.

How do we address this?

Global Spending to Increase on Fraud Detection, Cybersecurity and Big Data Analytics

Multiple studies and sources are coming to the same conclusion: organizations will be spending more on risk management and data analytics to keep up with growing fraud and business trends. This includes $9.2 billion spent on online fraud prevention services, $170 billion spent on cybersecurity and over $200 billion spent on big data analytics globally by 2020.

With payment and online fraud continuing to increase globally, Juniper research predicts annual spending on online fraud detection services across eCommerce merchants and financial institutions will reach $9.2 billion worldwide by 2020. This is just one aspect of risk management that online merchants and financial institutions have to deal with. Gartner estimates the global cybersecurity market exceeded $75 billion in 2015.

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IBM Acquires European Payment Fraud Vendor IRIS Analytics

In mid-January IBM announced the acquisition of IRIS Analytics, a data modeling and analytics provider based in Germany that focuses on risk management for issuers and payment providers. This is IBM’s second acquisition in risk management for the financial services industry following their 2013 purchase of Trusteer.

IRIS Analytics was founded in 2007 and provides a data modeling and analytics platform with real-time risk scoring to support payment card and mobile payments. The acquisition will bolster IBMs fraud prevention and eCommerce services, complimenting their IBM Security Trusteer Advanced fraud protection solutions.

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The Fraud Practice Offers Free White Paper and Webinar on Custom Modeling & Analytics for eCommerce Merchants

getting most out of consumer authentication with mobile data

The Fraud Practice is hosting a webinar at 1pm ET on December 4th accompanying the new white paper titled “Enabling Custom Modeling & Analytics for The Modern eCommerce Merchant” which will be released the same day. 

The eCommerce market has continued to mature over recent years to the benefit of both consumers and businesses that transact in this channel. As a result new markets and services have been created while existing markets have evolved and grown. Not long ago, any business seriously considering custom models would have found it expensive, timely and somewhat difficult to put in place, so the concept of implementing custom models was left for large financial and retail organizations that had the resources to afford the cost and teams that could support the models. This is no longer the case today, however, as the maturation and growth of these services has made custom modeling and analytics much more accessible and attainable to a far greater population of eCommerce merchants.

All who register for the live webinar will automatically receive a copy of the new white paper from The Fraud Practice prior to the webinar start time.

Register for the webinar and white paper

More information about the white paper

 

Differentiating Data Analytics from Data Analysis

In an interview with ZDNet, Josh Sullivan, leader of the data science and analytics practice at Booz Allen Hamilton, discusses the importance of human analysis complementing machine analytics. While the focus is on big data for any application, the discussion and points made provide insights and important considerations very relevant to modeling and analytics for fraud and risk management.

While card issuers and financial institutions have long employed modeling and analytics as a fraud prevention technique it is now becoming more common among eCommerce merchants. As a result we are seeing much more talk about machine learning, neural networks and advanced statistical models in the risk management marketplace. Although custom analytic modeling risk services require sophisticated platforms and technology, the reality is that a human element is required to ensure these services continue to run effectively.

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