Tackling The Problem Of False Declines To Drive Business Growth

Tackling the problem of false declines

Digital transformation is non-negotiable for today’s global retailers. They must meet growing market demands for seamless omnichannel experiences in order to drive customer loyalty and success — or risk falling behind the competition. But fraudsters want in on the action too. Tackling this rising challenge without impacting the end user is something many retailers continue to struggle with.

A recently released report from Aite Group reveals that 62% of US merchants have seen false declines increase over the past two years.1 The answer to minimizing false declines is a layered, data-centric fraud prevention powered by machine learning.

Fraud On The Rise

Online sales continue to soar as consumers jump between mobile devices, laptops, and desktops to find what they’re looking for. The vast majority (75%) of US merchants responding to Aite’s report revealed that over half of their sales in 2018 were made via internet channels. Yet where there is money and users, there will also be fraud. The introduction of EMV cards in the US has accelerated the shift from card-present scams using counterfeit plastic, to card-not-present (mainly online) fraud.

The impact on customers, and the retailers they shop with, is growing. Some 3.3 million consumers bore some liability for fraud in 2018, nearly three times as many as in 2016, while their out-of-pocket fraud costs more than doubled over the period to $1.7 billion, according to Javelin Strategy & Research.2

Getting Checks Right

This puts an increasing burden on in-house risk and fraud teams. Yet current tools don’t seem to be up to the job. The rising level of false declines indicate fraud prevention platforms are unable to differentiate between malicious and legitimate behavior. This could result in a ‘cure’ worse than the disease — fraud losses continue but are compounded by customer attrition. Tackling this challenge will be increasingly important to business success over the coming years. According to another Aite report, millennials are far less forgiving about false declines than older shoppers, with over half (59%) claiming they would be very or somewhat likely to leave their financial institution due to a credit card false decline.3

Another key indicator that things aren’t going to plan is the fact that 66% of merchants perform manual reviews on at least half of all sales transactions, according to the new Aite report. Half of these merchants say that over 70% of those transactions are subsequently approved. This tells us that better fraud tools would have automated the decisioning process, saving the organization significant sums in administrative overheads and freeing up fraud analysts to focus on more important tasks.

The Answer Is Here

So what’s the answer? According to Aite Group, merchants need a layered approach to fraud prevention, with a focus on minimizing customer friction. It recommends capabilities including:

· IP address verification

· Email address verification

· Customer ID verification

· IP/email address comparison for geographic similarity

· Device identity solution

· Machine learning models

· In-house and outsourced fraud scores

· Chargeback protection service

The good news is that Simility has most of these capabilities already integrated into its Adaptive Decisioning Platform, and is adding chargeback protection and risk scores soon, via its PayPal integration.

The Adaptive Decisioning Platform pulls data from a variety of sources for real-time analysis. It combines manual rules written easily by analysts in plain English with powerful machine learning models to pick out hidden fraud patterns. Even better, the platform continues evolving as these patterns change over time.

It’s part of the reason why Aite Group this year named Simility as one of just three best-in-class fraud and AML machine learning (ML) vendors.4 With our machine learning-powered platform in place, organizations have peace-of-mind that they’re stopping fraud in its tracks, without impacting the user experience or escalating too many transactions for manual review.

It’s a foundation on which today’s merchants can build solid business growth.

To learn more about how Simility’s Adaptive Decisioning Platform can help your business reduce false declines to drive conversion, reduce friction, and improve operational efficiency, schedule a demo now.

1. Aite Group, July 2019, https://aitegroup.com/report/e-commerce-conundrum-balancing-false-declines-and-fraud-prevention

2. Javelin Strategy & Research, March 2019, https://www.javelinstrategy.com/press-release/consumers-increasingly-shoulder-burden-sophisticated-fraud-schemes-according-2019

3. Aite Group, May 2017, https://www.aitegroup.com/report/combating-false-declines-through-customer-engagement

4. Simility, March 2019, https://simility.com/blog/celebrating-industry-recognition-as-a-best-in-class-ml-vendor/

Chirag Vaya

Chirag Vaya

As a product manager, Chirag leads the PayPal integrated line of Simility products. Having experience in building data analytics products, he believes in taking a data-driven methodology for solving problems. He has built products and solutions targeted towards solving fraud problems including stolen cards, account takeover, and new account origination for large enterprises and small-medium businesses.
Chirag Vaya

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