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?

Fraud Attacks, Losses and True Cost of Fraud All Increased in 2021

According to a recent study, eCommerce merchants in the US have experienced a 140 percent increase in fraud attacks relative to 2020, while every dollar associated with missed fraud costs organizations $3.60 on average, up 15 percent from before the pandemic.

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Five Tips for Increasing Speed and Performance with Manual Reviews

 Fraud and risk management strategies tend to focus so much on automated risk decisioning that improving manual review performance is often an afterthought. Consider the cost savings and increased revenue an organization could realize by cutting average order review times while also reducing sales insults and missed fraud on reviewed orders. This is why improving performance of manual reviews is at least equally important as efforts to reduce order review rates.

Here are 5 tips to increase speed and performance with manual review.

Order Risk Reviews Should Also Focus on Friendly Fraud, Refund Fraud and Abuse

It is understood that manual order reviews are a critical component of fraud prevention strategies, but like risk strategies overall, manual reviews tend to focus on third party fraud and often neglect other forms of fraud and abuse. The harsh reality is that fraud is a broader problem than identity and third party payment fraud. Since the onset of the pandemic, other forms of fraud, such as refund and promotion abuse, have increased. Manual reviews need to broaden the scope accordingly.

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Featured White Paper: E-Commerce and Omni-Channel Fraud Following the Digital Acceleration

This white paper examines the trends and common themes associated with merchants who thrived versus merely survived 2020 and the lasting changes on eCommerce and omni-channel retail. This includes a look at differences in risk management strategy architecture, readiness to scale and effective communication between the digital and physical worlds to support a true Unified Commerce strategy.

Download your free copy of this new white paper.

Should Legacy Rules-Based Systems be Ruled Out as New Marketplaces Emerge?

The accelerations in eCommerce Delivery, Marketplaces and the gig economy have companies scrambling to make better decisions, faster.  Model-based applications are being applied to address wider areas of fraud, as they can detect threats more precisely and efficiently. 

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The Rise of Machine Learning and AI in the Fraud Prevention Industry

13% of organizations today have adapted Machine Learning and AI into their fraud detection protocols. Another 25% of organizations plan on converting from a rule-based system within the next two years.

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Fraudsters Raised the bar in 2020 Increasing the Average Fraud Purchase Value to Over $700

The average order value during the Black Friday weekend was up 64 percent year-over-year, but during the entire months of October and November the average order value of fraud attempts was up 70 percent.

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More than 36 percent of Consumers Admit to Friendly Fraud

More than 36 percent of consumers admitted to falsely claiming a transaction was unauthorized or fraudulent while 31 percent falsely claimed a product never arrived, arrived damaged or was unsatisfactory, according to a recent study. While there is overlap of consumers who have made each of these false claims, this represents a meaningful share of consumers who knowing and willingly committed friendly fraud.

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