Outseer™
Risk Engine

More Fraud Detection,Less Intervention

Contents
Powering the Decisions That Protect Your Business .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 3Stop Fraud, Not Customers  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 4The Road to Results: Outseer Risk Engine .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  5
Analysis Across a Wide Array of Data Inputs  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 5Performance Enhancements .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 7The Risk Scoring Process  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 8See How It Runs .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 9A Modern Machine Learning Approach  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 9Why Our Algorithm Works So Well for Outseer Fraud Detection Products  .  .  . 9Why the Outseer Algorithm Works Better for Us than Other Methods .  .  .  .  . 10
Self-Learning: Performance That Gets Better All the Time  .  .  .  .  .  .  .  .  .  .  .  . 11
Six Questions to Ask When You’reConsidering a Fraud Prevention Solution .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  12
Outseer Risk Engine, the Foundation Technology
and Science of Our Fraud Prevention Solutions  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  13
Outseer Fraud Manager .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 13
Outseer 3-D Secure .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 13

Outseer Risk Engine™ | 2

Powering the Decisions That Protect Your
Business
While others debate the merits of different technology models for fraud detection, we’re busy proving thevalue of ours. Outseer products, powered by the Outseer Risk Engine, achieve fraud detection rates as highas 95%, with transaction intervention rates of 5%.
More Fraud Detection, Less Intervention
Modern identity science meets predictive analytics. Outseer Risk Engine predicts what others can’t to deliver
the best fraud detection and lowest intervention rates in the industry.

Detection

Detection Rate
100%
90%

92% 91%
87%

80%
77%

70%

94% 93%

96% 95% 96% 95%

97% 97% 97% 96%

84%

18H2

74%

95%

19H1
19H2

60%

20H1

50%

Intervention

40%

5%

30%
20%
10%
0%

1%

Outseer Risk Engine™ | 3

3%Intervention Rate

5%

7%
Source: Outseer 3-D Secure.

Stop Fraud, Not Customers
The fewer genuine transactions are challenged in the process of blocking
fraudulent ones, the better for everyone. A low genuine:fraud ratio meansincreased convenience for customers, higher approval rates and ultimately,
higher revenue for you.
Genuine:Fraud Ratio 20H1
Only 1.8 genuine transactions are falsely stopped to block 1 fraud.

2.0

1.8

Genuine Transaction

1.5

1.0

0.5

0.0
0

1
Fraud Transaction
Source: Outseer 3-D Secure.

Outseer Risk Engine™ | 4

The Road to Results: Outseer Risk Engine
The Outseer Risk Engine drives outstanding fraud detection
performance through a powerful combination of diverse data
inputs and data science expertise for continual improvement.

Here’s what Outseer Risk Engine takes
into account.

Analysis Across a Wide Array of Data Inputs

Payment Activity
Payment activity includes details
about a payment such as theamount, currency andpayee account.

Outseer Risk Engine™ | 5

Using a variety of sources of information, Outseer
Risk Engine assigns a unique risk score to every
digital transaction. The risk score and the risk
policy set by an organization together determine
whether a user is challenged with step-up
authentication. But a risk score is only as good as
the data that goes into it.

Non-payment Activity

Cross-Channel Information

Non-payment activity includes
details such as login, time of day,password changes andprofile updates.

Being able to take into accountfraud data from multiple channels(web, mobile, call center) has
been shown to boost fraud
detection rates.

Geolocation
Matching different location
attributes against one anothercan uncover signs ofsuspicious activity.

Outseer CaseManagement Feedback
Outseer Risk Engine can modify futurerisk predictions based on what itlearns from case investigations
and whether a flagged activity
is marked as genuine
or fraud.

Here’s what the Outseer Risk Engine takes into account.
To assign the most accurate risk score to detect fraudulent activity, theOutseer Risk Engine takes as many factors as possible into consideration.

Mobile Activity

Device Profile
Outseer Risk Engine analyzes thecharacteristics of the device such as
IP address, browser characteristics,
screen resolution characteristics
and indicators of malware
infections.

Outseer Risk Engine™ | 6

Mobile activity includes mobile
device details such as mobile
geolocation and Wi-Fi MAC address.
Outseer Risk Engine can detect
whether a device has been jailbroken
and whether a mobile app isrunning in an emulator as
opposed to on an actual
mobile device.

IP Information
Data about a device’s IP address
can be used to flag suspicioustransaction traffic.

Third-Party Risk Indicators
Data from third-party sources suchas other anti-fraud tools and yourorganization’s own internal intelligencecan be imported into Outseer Risk
Engine to be considered for
additional indicatorsof risk.

Outseer Global Data Network™
Our consortium of globallyshared, cross-industry fraud andtransaction data creates avaluable source of data forrisk scoring.

Performance Enhancements
Outseer Global Data Network: Sharing Data to Prevent Fraud
The Outseer Global Data Network receives data from Outseer customers around the world. As a source ofdata for the Outseer Risk Engine, the network provides direct feeds on fraud threats.

5 million/month

Transactions flagged as fraudulent by the
Outseer Global Data Network$350 million/year
Fraud savings attributed toOutseer Global Data Network

Source: Outseer

44,000$

Savings per month a major
U.S.-based financial institution
realized by using cross-channel
data from their call center toincrease fraud detection on web
and mobile banking channels
Source: Outseer

Outseer Risk Engine™ | 7

Outseer’s Ecosystem Approach:
Our Expertise, Applied to Your Data
Elements
The Outseer ecosystem approach enriches
the Outseer Risk Engine risk score
calculations by enabling you to include yourorganization’s own data inputs from internal
and third-party channels. The Outseer Risk
Engine consumes this additional data, learns
from it, and incorporates it into the risk model
to enhance risk scoring. This helps you gain
more value from your investment in otherfraud prevention tools while centralizing fraud
management.

The Risk Scoring Process
Risk scoring with the Outseer Risk Engine begins with gathering facts from the data inputs and continues
through the steps below. The risk engine learns from case markings feedback, charge-backs andauthentication results and applies that learning to the next risk scoring event.

1. Data Gathering

2. Profile Building

3. Predictors Generation

The Outseer Risk Engine collects
data from diverse inputs, enriches
it and applies a uniform data
format to it, and then uses thedata to analyze the risk presented
by a transaction.

Profiles are sets of accumulated
historical data and statistics about
the user, IP address, device andother relevant entities. Using these
profiles, the Outseer Risk Engine
can distinguish between normaltransaction behavior and abnormal
behavior that may suggest fraud.

Predictors are data variables in therisk model assumed to correlate withfraud. They’re generated from facts
about the current transaction as well
as from historical data from profiles.
The better the predictors, the moreaccurate the prediction of fraud.

1• 0.25% of events will get a score of 900-1000

Preliminary
Risk Score

3
Predictors
generation6
selflearning

5

2

normalized
RISK Score

Profilebuilding

4. Preliminary Risk Score

5. Normalized Risk Score

6. Self-Learning

The Outseer Risk Engine generates
a preliminary risk score basedon the probability of fraud given
the predictors that are present.
All data variables are taken into
account in this preliminary score,with each one weighted based on
its relevance.

Risk scores are normalized to alogarithmic scale from 0-1000,
using linear interpolation to convert
the risk score to a common scale.
Normalization gives you morepredictability and control (see sidebar).

The Outseer Risk Engine learns
automatically and in real time, andthen develops new patterns basedon what it learns. Its self-learning isbased on anomaly patterns detected
and real-world feedback received
about step-up authentication results
and case management feedback.

Outseer Risk Engine™ | 8

Risk scores generated by the Outseer Risk Engine
gives you more control over the percentage oftransactions requiring intervention. In turn, thisgives you the ability to modulate staffing (bothcall center and anti-fraud center). It also brings
predictability to the impact on score-based
policies on the end user experience (intervention/
challenge rates and false positives).
Final Score represents fraud likelihood andcontrols the percentage of events withinscore bands:

4
DATA
gathering

Normalization of Risk Scores Puts
Control Where It Belongs: With You• 1% of events will get a score of 700-1000
Normalized Risk Score
Lower Bound

Upper Bound

Percentile

Cumulative
Percentile

900

1000

0.25%

0.25%

800

900

0.25%

0.50%

700

800

0.50%

1.00%

600

700

2.00%

3.00%

500

600

2.00%

5.00%

400

500

5.00%

10.00%

300

400

10.00%

20.00%

200

300

10.00%

30.00%

100

200

20.00%

50.00%

0100

50.00%

100.00%See How It Runs
You’ve seen the results. You’ve seen the data that goes into the risk scores.
Now let’s take a look under the hood at the technology and science that enableaccurate scoring and constant improvements—and lead to outstanding results.
A Modern Machine Learning Approach
You may have seen fraud detection products that swear by combining three, four, or even more machinelearning algorithms.
Just remember: It’s about quality, not quantity. Outseer fraud detection products rely on one unique machinelearning algorithm with multiple patents. Our algorithm has served our customers extremely well in recent
years.

Why Our Algorithm Works So Well for Outseer Fraud Detection Products
Our proprietary machine learning algorithms and risk models can1,2,3:

Easily accommodate theaddition of new predictors,
which is crucial given the
rapidly changing nature offraud today

Quickly learn new fraud
patterns on smaller data
sets (e.g., when there’s lessfeedback available about
fraudulent vs. genuine
transactions)

Provide visibility into theparameters that contribute to
the final result

Just remember:

It’s about

QUALITY,

not quantity.

Outseer Risk Engine™ | 9

Why the Outseer Algorithm Works Better for Us than Other Methods
Many machine learning models are used for fraud
detection today, including neural networks. While thisoffers its own advantages, there are also limitations,too. For example, artificial neural networks (ANNs)
simply cannot provide information about the relative
significance of various parameters. This means
there’s no way to understand what contributed mostto the risk assessment, nor any way to visualize thecontributing factors. This leaves the call center teamunable to explain to customers why a transaction
was declined.
That’s a problem for ANNs as well as their moreadvanced counterparts, deep neural nets, or DNNs.
While DNNs shine when it comes to working withhuge data sets, ANNs have been shown to produce
inferior results for small sample sizes as fraud % isstill very small comparing to genuine transactions
the results might not be as accurate.

Outseer Risk Engine™ | 10

The machine learning algorithm implemented in the
Outseer Risk Engine offers advantages over othermodels because it:
Handles missing data very well
Provides a natural framework formixed numeric and categorical data
Is computationally very efficient
Is simple and helps you understand what
factors contributed most to risk scoresSelf-Learning: Performance That Gets Better All the TimeThe Outseer Risk Engine is a self-learning engine that automatically adjusts to predict, and protect against,
future attacks based on three types of feedback:Data Science Team:
Driving Performance

Case management

Chargeback data

Authentication results

Outseer Fraud Manager andOutseer 3-D Secure create cases
for investigation, and Outseer Risk
Engine automatically modifies
future risk predictions based on theinvestigations’ results.

The risk engine learns from
Outseer 3-D Secure data on
chargebacks and automatically
adapts its risk predictor weighting
to more accurately identify fraud.

Failed step-up authentications
automatically result inhigher risk scores for futuretransactions from the sameaccount with similar device
parameters. Likewise, successfulauthentications lower the risk
score in future transactions.

Outseer Risk Engine™ | 11

Machine learning may conjure up imagesof black boxes, but it takes more thantechnology to deliver the kind of performancethat the Outseer Risk Engine does. Outseer
has a dedicated, expert team of data
scientists who bring expertise and experience
to bear on interpreting data, evaluating
patterns and modeling accordingly.

Six Questions to Ask When You’reConsidering a Fraud Prevention Solution
Now it’s time to cut through the machine learning hype and get down to business. As you start to evaluate fraud prevention
products and the advantages of the machine learning models they offer, here are some key questions to ask potential vendors:

12

 hat is your fraud detection rate today,Wand what is your goal?
 hat is your average customer
W
intervention rate today, and what is your
goal?

3

	
What is your average false positive rate
today, and what is the impact on yourbusiness?

Outseer Risk Engine™ | 124

 hat data sources and details does yourW
solution consider in calculating transaction risk
scores?

5

 ow does your solution learn over time toHidentify emerging fraud patterns and provide
better risk assessments?

6

 oes your solution learn over time using caseDresolution feedback?

Outseer Risk Engine, the Foundation Technology
and Science of Our Fraud Prevention Solutions
Outseer Risk Engine is foundational to our offerings thanks to its unrivalledaccuracy. By seeing what others can’t, the risk engine powers the solutions
that protect your business—including:Outseer Fraud Manager

Outseer 3-D Secure

Outseer Fraud Manager relies on the Outseer Risk Engine to assign an accurate
risk score to every transaction, and intervenes only when the risk score warrants
it. The result: fraud detection rates as high as 95%, and intervention rates of 5%.
All while delivering a frictionless user experience.

Outseer 3-D Secure provides a consistent, secure online shopping experience
while reducing fraud loss and chargebacks. By leveraging the Outseer Risk
Engine, Outseer 3-D Secure is able to help credit and debit card issuers andpayment processors dramatically reduce chargeback losses from CNP fraud.

• Increases fraud detection rates across all digital channels without adding
friction• Gain your customers trust and loyalty (“top-of-wallet”) so they choose yourcard to transact with—every time!

• Optimizes existing investments in anti-fraud tools• Grow profitability by reducing fraud losses, reducing chargebacks andlowering operational costs associated with fraud investigation• Unlocks internal business intelligence to enhance risk assessment• Centralizes fraud management like never before possible

Outseer Risk Engine™ | 13

• Increase transaction approval rates that drive up revenue and help you growyour business1. T heodoridis and Koutroumbas, Pattern Recognition, 4th Edition (2009), Chapter 2, “Classifiers Based on Bayes Decision
Theory,” (in particular section 2.5.7, “The Naive-Bayes Classifier”
2. D uda, Hart and Stork, Pattern Classification, 2nd Edition (2001), Chapter 2, “Bayesian decision theory” and Chapter 3,“Maximum likelihood and Bayesian estimation”
3. H
 an, Kamber, Data Mining: Concepts and Techniques, 3rd Edition (2005), section 6.4 Bayesian Classification 310

About Outseer
Outseer empowers the digital economy
to grow by authenticating billions oftransactions annually. Our payment andaccount monitoring solutions increase
revenue and reduce customer friction forcard issuing banks, payment processors,and merchants worldwide.

Leveraging 20 billion annual transactions
from 6,000 global institutions contributing
to the Outseer Data Network, our identitybased science delivers the highest fraud
detection rates and lowest customer
intervention in the industry. See what others
can’t at outseer.com©2021 RSA Security LLC or its affiliates. All rights reserved. RSA, the RSA logo and Outseer are registered trademarks or trademarks of RSA
Security LLC or its affiliates in the United States and other countries. All other trademarks are the property of their respective owners. RSA
believes the information in this document is accurate. The information is subject to change without notice. Published in the USA.06/21