This document provides an overview of card-not-present (CNP) fraud challenges faced by merchants. It notes that CNP fraud losses are predicted to exceed $7 billion by 2020 in the US alone. Machine learning and artificial intelligence are playing a crucial role in combating CNP fraud by allowing real-time transaction monitoring and analysis of purchasing patterns. The document promotes QPS's solutions for CNP fraud prevention including their CNP Secure product, which uses advanced machine learning algorithms and open API integration to help merchants increase fraud detection rates and reduce chargebacks.

1. QPS WHITEPAPER
Are Merchants Losing The CNP
Fraud Battle?

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TABLE OF CONTENTS
Introduction ______________________________________________________________3
What Brings The High Fraud Tide To CNP Shore?_______________________________ 4
CNP Fraud – A Sharp Knife Slicing Away Merchants Profit_________________________ 5
Machine Learning Playing Crucial Role________________________________________ 5
QPS Robust Solutions Combating Chargeback _________________________________ 6
QPS Empowering Merchants________________________________________________ 6
QPS Effectiveness Snapshot – Proven Track Record _____________________________ 8
Open API Integration ______________________________________________________ 9
Conclusion _____________________________________________________________ 10
Resources _____________________________________________________________ 11
Connect with QPS _______________________________________________________ 12

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INTRODUCTION
The number of global internet users have reached 4 Billion in 2018 and are predicted to cross
6 Billion by 2022, bringing the connected devices on the Internet close to 50 Billion by 2020.
Growing internet usage is the breeding ground of cybercrime which is predicted to cost the
world $6 Trillion annually by 2021, posing the greatest threat for the global financial services
industry.
Retailers are estimated to lose $130 Billion to card-not-present (CNP) fraud globally during
the next five years. In the U.S. alone, CNP fraud losses are predicted to exceed $7 Billion by
2020. Threats have increased manifold as fraudsters are getting more sophisticated in their
attacks by using advanced methods.
As rising CNP fraud is throwing a tough challenge, Account Takeover Fraud (ATO) and New
Account Fraud (NAF) are also catching up fast and posing an even bigger threat for the online
payments ecosystem. ATO fraud has reached a four-year high in 2017 resulting in a $5.1
Billion loss for merchants.

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What Brings The High Fraud Tide To CNP Shore?
E-commerce is becoming a preferred marketplace as it is predicted to touch $11 Trillion by
2023 and the share of CNP transactions will growing accordingly. The e-commerce
expenditure is destined with intensified growth which will attract more fraudsters as they
always prefer the vulnerable path, and after the introduction of EMV chip cards, they are
focusing more on such payment channels.
CNP fraud remains the most prevalent fraud type and making huge losses to merchants,
particularly in countries with high EMV migration rates. The challenge faced by retailers with
CNP channel is to authenticate the cardholder as it cannot be done using the physical POS
procedure hence, an alternative approach to authenticate the cardholder is required.
Yahoo and Equifax data breaches during the past years have brought a fraud storm to the
CNP world, as fraudsters are illicitly harvesting on the breached data, putting millions record
containing personal information at risk. The stolen data is used for credential stuffing with the
help of online bots to test the login credentials on numerous websites as well as facilitating
account takeover attack. When it comes to payment cards, card-present fraud has decreased
over the last couple of years; especially after EMV implementation but card-not-present
(CNP) fraud continues to rise. The graph below is highlighting the growth of CNP credit card
losses during the last five years.

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CNP Fraud – A Sharp Knife Slicing Away Merchants’ Profit
Merchants are the immediate victim of the CNP fraud aftermath.
They lose the merchandise ordered by the fraudsters, refund
money to the customer whose payment information was used by a
fraudster, and further pay associated chargeback fees. This can
have large ramifications for merchants, including the time and cost
to examine the chargeback, dealing with the customer’s frustration
due to canceled purchases and ensuring that this customer is not
lost.
Quatrro Processing Services (QPS) has been serving merchants with an ever-evolving
approach for preventing fraud and chargeback.QPS’ innovative approach of fraud prevention
driven by Artificial Intelligence (AI) and Machine Learning (ML) helps merchants to identify
and prevent the risk of fraud. QPS’ smarter fraud mitigation strategy helps merchants to catch
the fraud before the product is shipped and thus saves on fraud and prevents the chargeback.
Machine Learning (ML) Playing a Crucial Role
Use of ML to detect anomalous behavior is an emerging and effective method, enabling
faster detection of suspicious pattern. Designing, implementing and upgrading the fraud
detection rules can be facilitated in real-time.
AI complements ML to design and implement algorithms which help in understanding
customers’ pattern from past cases and automate the threshold limiting for real-time analysis.

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AGGREGATOR MODEL - QPS’ aggregator model not only helps merchants in fraud
prevention but also helps in effective chargeback control with its highly efficient amalgamation
of advanced machine and human intelligence. It is an efficient E2E (end-to-end) proactive
chargeback management and CNP fraud prevention solution, which is driven by real-time
transaction monitoring.
REAL-TIME TRANSACTION MONITORING–Real-time monitoring strategy acts as a super
quick model providing a resolution within 300 million seconds by analyzing multiple
authentications including advanced Big Data analytics.
BATCH MODE PROCESSING - Large data volumes are processed efficiently with QPS batch
mode processing which provides in-depth transaction analysis. With this method,
comprehensive verification is achieved in resolution time between 30 minutes – 24 hours
through human eye intervention.
CHARGEBACK PROPENSITY RATE (CPR) – QPS’ proactive chargeback prevention through
CPR eliminates the dimension of fraud, threatening businesses. For generating CPR, a rating
from HIGH/ MEDIUM/ LOW is assigned to each work order in conjunction with the system and
human intelligence.
QPS has been helping businesses and retailers in mitigating CNP fraud losses by
empowering them with robust fraud detection solutions, layered with comprehensive data
analytics services.
Below is the ever-evolving CNP fraud & chargeback prevention model by QPS.
QPS Empowering Merchants
QPS Robust Solutions Combating Chargeback

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E2E CNP Fraud & Chargeback Management
QPS’ solutions consist of cutting edge technology supported by a complete back-office
services function ensuring the reduction in false positives. BIG Data and social media
analytics help in evaluating rules/algorithms, assessing fraud patterns and trends, reducing
chargeback and friendly fraud. An E2E fraud protection solution has been tailor-made to
enhance order acceptance rates combating chargeback before service deliveries & order
shipments.
CNP Secure- An Al-enabled Solution For e-commerce Fraud Prevention
QPS’ smarter fraud detection solution - CNP Secure provides an effective chargeback and
fraud protection solution. AI-driven CNP Secure is integrated with vital elements for higher
accuracy and real-time prevention as given below:
 Advanced Access Control Engine
 Core Decision Engines For Managing Rules & Scoring Modules
 Advanced ML Algorithms For Fraud Scores And CPR Ratings
 Open API For Easy And Fast Integration
 Next Level Business Logic Layer Module For Fast Decisions
Facilitating Payments Security with Open API
Advanced machine learning through open API and human eye intervention enhance the
capability of fraud detection, reduce chargeback and boost profits. Analysts derive the fraud
score by analyzing a powerful database highlighting customer's behavior on specific
merchants and then negating the chargeback propensity termed as the Chargeback
Propensity Rate (CPR).
QPS’ model of fraud prevention helps you to identify the
propensity/risk of a fraudster committing fraud and protect your
business by deploying a multilayered fraud prevention strategy.

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QPS CNP fraud prevention services travel through the API channel, making it feasible for
clients to integrate their transaction data quickly and conveniently. The API system is a
technology marvel through which QPS can share the service response within a minute for a
real-time transaction and within 48 hours in case of bulk mode.
The futuristic design of API integration allows machine learning models to nab fraudsters at
every turn and always be one step ahead of them. APIs allow the data to be easily embedded
or interwoven throughout, to help ensure a smooth and integrated user experience.
“AI and machine learning driven CNP fraud fighting models are yielding 30% increase in fraud
detection rate over previous approaches. An amalgamation of AI and API, as a vital extension
of FinTech is an ideal approach to provide the level of protection that today’s business
demands. QPS with its technology advances and futuristic fraud fighting strategies, provide
merchants the confidence and security layer to fight CNP fraud and chargeback. “
- Manu Sharma, AVP Expert Systems, QPS
QPS Effectiveness Snapshot – Proven Track Record

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With API integration in place, QPS is able to make live data accessible for real-time
transaction monitoring and prevent fraud before it happens.
QPS API is complemented with unique Blocking and Bypass Engines, which create extensive
fraud management efficiency for debit/credit card issuers. It helps in monitoring transactions
for the specific merchant and terminal levels or at MCC and country levels. This Increases
CNP transaction approval rate while reducing processing and customer service time through
operational efficiencies and mitigating the risk of customer attrition.
Meticulously Advanced BIG Data Analytics Services
It includes comprehensive analytics services for evaluating rules and assessing fraud
patterns which will help in improving decision accuracy and ensuring fraud loss reduction,
covering the following:
 Identifying fraud trends and patterns
 Creative, interactive analysis to uncover patterns that are most likely to be indicative of
fraud
 False-positive review

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Conclusion
QPS plays the role of an ideal aggregator with highly efficient scoring and identity data
solution along with email verification coupled with AI and human intelligence. QPS highly
skilled resource pool provides expert analysis to combat CNP fraud by employing a multi-
layered approach for identity verification and fraud prevention. We will protect merchants’
brand reputation and their customer's trust with our 24*7*365 fraud mitigation approach.
“Embracing AI and robotics are vital for achieving our goal of e-commerce fraud prevention
technologies advancement. With fraud strategies evolving, we need to work together to make
sure that we protect businesses against rising fraud and chargeback with a smarter approach
driven by machine learning and open API.”
- Ankit Maharaj Singh, VP, QPS

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Resources
https://www.thepaypers.com/expert-opinion/card-not-present-fraud-are-you-even-aware-of-your-
options-to-combat-cnp-fraud-/773006
https://www.clear.sale/infographics/cnp-fraud-is-skyrocketing
https://www.paymentscardsandmobile.com/card-payments-rise-to-24-of-total-e-commerce-spend/
https://cybersecurityventures.com/cybersecurity-almanac-2019/
https://cybersecurityventures.com/cybersecurity-almanac-2019/
https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016/
https://www.globalbankingandfinance.com/the-migration-of-card-not-present-fraud-in-europe-and-
how-to-fight-it/
https://www.printful.com/blog/the-basics-of-ecommerce-fraud-what-is-it-and-how-to-manage-it/

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