Using ML to detect and prevent fraud without compromising user experience - FSV302 - New York AWS Summit

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Using ML to detect and prevent fraud
without compromising user
experience
Christopher Marsh-Bourdon
Principle Solutions Architect
Amazon Web Services
F S V 3 0 2
Justin Fox
Head of Platform Innovation
NuData Security

Agenda
What is NuDetect?
Layers of threat intelligence
Big-data processing
Let’s talk DataOps
Next steps

Related breakouts
AIM304 – Machine learning for developers & data scientists with Amazon
SageMaker
Cyrus Vahid, AWS
AIM306 – Fraud detection using machine learning with Amazon SageMaker
Cyrus Vahid, AWS
AIM309 – Setting up custom machine learning environments on AWS
Shashank Prasanna, AWS

Let’s start with our history
NuData Security was born in the AWS Cloud
• Early adopters in 2007, on Amazon EC2 Classic
• As the cloud innovated we leveraged the advances
• VPC? Check. Config? Check. Lambda? Check.
Targeted identity verification to reduce fraud
• E-Commerce, Financial Institutions
• Focus on consumer experience, #frictionless
• Powered by machine learning

What does the landscape look like?
of payment executives at FIs believe that the evolution of
digital as a channel adds significant risk of fraud180%
of credit losses and 5 percent of charged-off accounts are
due to credit application fraud using synthetic identities3
Cybercrime is more
sophisticated than
ever and the costs
are mounting
As banking moves toward a more
digital experience with increased
points of interaction, it is more critical
than ever to accurately identify your
real customers from fraudulent
attempts to manage risk
1/3 of US businesses have had customer data breached2
cost of cybercrime to US issuers by 20194
$2 trillion
20%
1. LexisNexis, "True Cost of Fraud,” 2017 2. Javelin 2017 State of Authentication Report
3. Auriemma Consulting Group, "Synthetic
Identity Fraud Cost Lenders $6 Billion In
2016," August 2017
4. Forbes, “Cyber crime costs projected to
reach $2 trillion by 2019," 2016

Why do we care?
Accurate authentication is critical, but it requires careful
balancing of security needs with a great user experience
#1
Identity verification is
among the top three
challenges facing financial
institutions1
36%
increase in incidence of
account takeover since 2015
and a 60 percent increase in
losses2
74%
of financial institutions state
that improving the
consumer experience is an
important component in a
business case for a new
fraud solution3

We combine layers of security
Device, connection,
and location
identification
Trust that the real consumer is
using the device.
Behavioral analytics
Continuously verify the
consumer. Trust the
behavior.
Passive (invisible)
biometric verification
Trust the consumer based on
natural behaviors and
sensory inputs.
Real-time trust
consortium
Aggregated, network-level data
from all behavioral interactions.
Trust the consortium.

Intro to our architecture
Systems evolve
Monolith
Microservices
Containers
Functions
Leverage managed services
Lower operational overhead
Acts as an interface between systems
Key principles
Creating a DevOps culture, teams own stuff
You made the model, you own it (end to end)

NuDetect architecture
Corporate data
center
AWS Cloud
Amazon CloudFront
AWS PrivateLinkAmazon VPC
AWS Cloud
AWS Global Accelerator Elastic Load
Balancing
Amazon EC2
AWS Lambda
Amazon ECS
Connectivity options Real-time scoring engine

Microservice architecture
AWS Cloud
Real-time device intelligence
Real-time passive biometrics
Real-time trust consortium
Amazon DynamoDB
Amazon RDS
Amazon ElastiCache
Amazon API Gateway
Elastic Load Balancing
Elastic Load Balancing Amazon EC2
Lambda
Amazon ECS

How much data do you need?
Big data is just another word for data hoarder
Every service generates data
Telemetry data/metrics
System logging
Application logging
Performance data
Let’s be real: More data equals more problems
Compliance
Regulations
Privacy

Evolution of big data at NuData (part 1)
Data operations monitoring
Elastic Load
Balancing
Amazon EC2
ElastiCache
Amazon S3
Amazon Redshift
1
12
2
Amazon EC2
Spot Fleet
Amazon SNS Amazon CloudWatch Amazon EC2 Auto Scaling
AWS Cloud
Data processing workflow
Cassandra

Event aggregation workflow
ElastiCache Amazon EC2 Elastic Load
Balancing
Amazon EC2
ElastiCache
Amazon S3
Amazon Redshift
1
12
2
Amazon EC2
Spot Fleet
Amazon SNS CloudWatch Amazon EC2 Auto Scaling
AWS Cloud
Cassandra

AWS Cloud
Event aggregation workflow
ElastiCache Amazon EC2 Elastic Load
Balancing
Amazon EC2
ElastiCache
Amazon S3
Amazon Kinesis
Data Firehose
Amazon Redshift
Amazon ES
1
12
2
Amazon EC2
Spot Fleet
Amazon SNS CloudWatch Amazon EC2 Auto Scaling

AWS Cloud
Amazon SNS CloudWatch
Amazon S3
Amazon Redshift
Kinesis
Data Firehose
Amazon ES
Amazon Athena

Understated benefits of the cloud
Minor changes in architecture can have large impacts
Everyone talks about security, elasticity, and paying for usage
Shift away from do-it-yourself on Amazon EC2 to free up time to
innovate
Every decision made has a feedback loop: the underlying AWS bill!
Achieving innovation velocity requires focus
Delegate undifferentiated heavy lifting to managed services
Provide educational programs and reward innovations
Enable teams to drive business value through culture

Practical implementation caveats
Meet your new friend: AWS CloudFormation
There are alternatives—pick your poison!
Integrate with AWS Service Catalog
Provide guardrails as needed
Collaborate and partner
DevOps, SysOps, security, many specialized skill sets
Focus on compliance requirements
Understand relevant regulations
Key principle
If you made the model, you should own it (end to end)
AWS CloudFormation
AWS Service Catalog
AWS Organizations

Powered by
machine learning
Online application origination
Growing issue as breaches expose more data
Uses synthetic identities to create accounts—or
sign up for credit cards online
How do you defend against synthetic identities?
Model development
We reduced model development time by 60
percent—the more we practice it, the better we
get!
Used a variety of AWS services

Develop common libraries
AWS Cloud
Machine learning development library workflow
AWS Cloud9 AWS CodeCommit
AWS CodeBuild
AWS CodePipeline Amazon S3
AWS CodeBuild
AWS Service Catalog AWS CloudTrailAWS Config CloudWatchAWS CloudFormation
1 2 3 4 5

Prepare a dataset
AWS Cloud
VPC
Amazon SageMaker
Dataset preparation workflow
CloudTrailAWS Config CloudWatch
1 2 3
Amazon Redshift Amazon Simple Storage
Service (S3)

Train a model
AWS Cloud
Amazon S3
Amazon SNS Amazon CloudWatch
SageMakerLambdaAWS Step Functions
Model training workflow

Host a model
AWS Cloud
Amazon SNS
LambdaAmazon API Gateway
Amazon CloudWatch
SageMakerDynamoDB
Real-time machine learning API

Sample code
@RestController
public class ExampleHandler {
private String modelName = "exampleModel";
private Model model;
private Parameters parameters;
private SageMaker service;
@Autowired
public ExampleHandler(SageMaker service, Model model, Parameters parameters)
this.model = model;
this.modelParameters = modelParameters;
this.sageMakerService = sageMakerService;
}
@PostMapping("/predictions")
public Mono<ServerResponse> predict(Counter counter) {
parameters.setParameter("velocity", counter.getVelocity());
model.setModelName(this.modelName);
return Mono.fromFuture(service.invokeAsync(model, parameters));
}
}

Next steps, directions
Evolution is key to long-term success
Need to be able to respond rapidly to new threat vectors
Need to be able to innovate rapidly with new technologies
Goal is to protect every device and every transaction online
Cloud is key, but how you use it matters
No silver-bullet solution, not off the shelf
Focus on core business, offload undifferentiated heavy lifting
Open the discussion, keep innovating
AWS Solution: Fraud Detection Using Machine Learning
Mastercard: Brighterion, Ethoca, NuData Security

Interested in NuDetect?
Mastercard is hiring!
Apply now at https://nudatasecurity.com/company/careers/
Join the conversation!
Follow us on Twitter: @NuDataSecurity
Follow us on LinkedIn: https://www.linkedin.com/company/nudata-security/
Looking to reduce consumer friction?
Contact a sales engineer at sales@nudatasecurity.com
Take a peek at our demos: https://nudatasecurity.com/solutions/demo/
Lots of options for preventing fraud:
NuDetect, ATO Protect, Smart Interface, Trusted Device

Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Christopher Marsh-Bourdon
Principle Solutions Architect
Amazon Web Services
Justin Fox
Head of Platform
NuData Security

Using ML to detect and prevent fraud without compromising user experience - FSV302 - New York AWS Summit

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