Voice of the Industry

Brick and Mortar navigates digital transformation

Wednesday 6 March 2019 09:48 CET | Voice of the industry

Rich Stuppy of Kount reveals the right approach to fraud prevention for brick and mortar merchants that have started to provide digital services

Traditional brick and mortar merchants are expanding beyond their four walls to engage with customers through mobile apps, kiosks, desktops, and other digital platforms. At the forefront of this digital transformation is the introduction and branding of trademarked native mobile apps supporting rich features for creating and managing accounts, earning loyalty points, providing reviews, engaging with customer support, other customers and more. While mobile apps for retail are nothing new, many of the first-generation apps are being replaced with apps supporting creative and elaborate digital interaction use cases. These new apps allow merchants and retailers, regardless of sector, to engage with customers in a digital environment, in order to build brand loyalty and engagement and drive towards greater monetisation with enhanced ease-of-use and personalisation.

This shift towards digital economy is fueling the growth of the mobile payments industry and it’s becoming a beacon for fraudsters to attack traditional brick and mortar merchants. In fact, The Mobile Payments and Fraud: 2018 Report stated that detecting fraudulent orders is one of the top three challenges for merchants in the mobile channel.

Card Present versus Card Not Present = Chargebacks

As brick and mortar merchants make this digital transformation and begin to accept card-not-present and mobile ecommerce, they become exposed to all types of fraud schemes and chargeback programmes that can cause disruption and large financial and brand loyalty losses.

When brick and mortar merchants experience fraud in their traditional card-present environment, the liability of loss is generally on the card issuer if the merchant supports EMV transactions. In a card-not-present (CNP) environment, however (online, mobile web, or mobile app), the liability for a fraudulent transaction now falls to the merchant. This places the merchant at risk of new fraud tactics, potential chargebacks, and greater financial losses.

With a new focus on creating digital accounts for their customers, traditional brick and mortar merchants are also exposed to all types of new fraud, including:

Account takeover: Gaining access to an established digital account using compromised credentials (username and password) allows a fraudster to take advantage of the value of that account. This may include using the saved payment method or loyalty points to make purchases.

Loyalty reward points fraud: Because reward points can work like cash, fraudsters identify weaknesses in the system and steal reward points to sell them.

eGift cards fraud: Considered low-hanging fruit, electronic gift cards are easily converted into cash, a key requirement for fraudsters. They sell them at a discount, with the merchant responsible for the resulting chargebacks and any merchandise or services provided for the value of the gift card.

Promotion fraud: Launching a promotion can often capture the attention of fraudsters who are skilled at identifying ways to get around policies or offer limits.

Approach to fraud protection

Brick and mortar businesses navigating towards a digital transformation need to deploy a fraud strategy that is multi-layered and specifically accounts for card-not-present fraud.

An underpinning technology for stopping CNP fraud is machine learning. Machine learning combines data, context, and feature engineering to allow organisations to evaluate the risk of a particular digital interaction or purchase. Machine Learning, a form of artificial intelligence, allows fraud prevention solutions to “learn” on their own and continually improve results. In order to stop a card-not-present payment, there are two critical types of machine learning that, when combined, provide the best fraud prevention foundation.

Unsupervised Machine Learning. Unsupervised learning does not require outcomes, so it can learn without waiting for the completion of a three-month chargeback reporting cycle. This type of learning often relies on clustering, peer group analysis, breakpoint analysis, or a combination of these. This enables fraud prevention solutions to detect patterns and anomalies rapidly within extremely large sets of data.

Supervised Machine Learning. Supervised learning uses outcome-labelled training data sets to learn. Models include neural networks, Bayesian classifiers, regression, decision trees, or an ensemble combination. Massive amounts of data run through defined models to assess risk outcomes.

Brick and mortar merchants that deploy a mobile app need to account for a new world of risk through digital fraud attacks. There are great benefits to investing in digital engagement channels, however, with those opportunities comes risk. By addressing fraud with a holistic strategy, merchants can authenticate a user, identify fraudulent behaviour, and stop fraud before it influences the bottom line and diminishes the merchant’s brand. By building a level of fraud prevention in their mobile apps, brick and mortar merchants are empowering decision makers with data to make informed decisions and to mitigate fraud before it impacts the businesses’ bottom line.

This editorial was first published in the Web Fraud Prevention, Identity Verification & Authentication Guide 2018-2019.The Guide covers some of the security challenges encountered in the ecommerce and banking, and financial services ecosystems. Moreover, it provides payment and fraud and risk management professionals with a series of insightful perspectives on key aspects, such as fraud management, identity verification, online authentication, and regulation.

About Rich Stuppy

Rich L. Stuppy is the Chief Customer Experience Officer at Kount. For more than a decade Rich has been involved in developing fraud mitigation, compliance, and big data strategies. Rich came to Kount after 14 years with a Fortune 50 retailer. His background in enterprise-class systems, machine learning, and analytics has shaped Kount’s technology into an industry leading platform helping clients scale their business while at the same time reducing fraud, risk, and loss.

About Kount

Kount’s award-winning fraud management, identity verification and online authentication technology empowers digital businesses, online merchants and payment service providers around the world. With Kount, businesses approve more orders, uncover new revenue streams, and dramatically improve their bottom line all while minimizing fraud management cost and losses and protecting consumers. Through Kount’s global network and proprietary technologies in AI and machine learning, combined with policy and rules management, companies frustrate online criminals and bad actors driving them away from their site, their marketplace and off their network.

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Keywords: brick and mortar, merchants, Kount, fraud prevention, Rich Stuppy, machine learning, CNP fraud
Countries: World