Using Graphs to Take Down Fraudsters in Real Time

1. © 2022 Todo1 Services, Inc. All rights reserved.
Using Graphs to Take Down
Fraudsters in Real Time
Edgar Osuna, Ph.D.
Chief Data & Analytics Officer

2. © 2022 Todo1 Services, Inc. All rights reserved.
Agenda
• About TODO1
• Use Case: Digital Banking Fraud Detection (Real Time)
• Why is this problem hard ?
• Why Neo4j ?
• Operational Footprint
• Challenges and next steps
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3. © 2022 Todo1 Services, Inc. All rights reserved.
About TODO1
• Mission to humanize the interaction between customers and their financial
institutions through our Digital Banking solution and components
• Born from DNA of leading Latam Banks and operating for 20+ years
• 500+ Employees in Latam and US
• Processing 7.5+ Billion transactions per year for 15+ million customers
• Relatively new to the Public Cloud and Machine Learning landscapes
• iuviProfiler is the component designed for Fraud Prevention and was
awarded a Graphie award in 2021 (Pioneer – AI for Fraud Detection in
Banking)
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• Banking digital channel
unauthorized interactions
executed on behalf of a
customer using valid
credentials
• Typical digital interactions
include login, password
change, personal information
update, transfers and all types
of payments, loan
applications, etc.
Digital Banking Fraud

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Use Case: Digital Banking Fraud Detection (Real Time)
01
02
M A C H I N E L E A R N I N G
m o d e l s a n d
a l g o r i t h m s
Analysis
SCORE RECOMMENDATION
0 - 1000 Decline –
Authenticate –
Review
Legitimate
Profile
Realtime activity

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Use Case: Digital Banking Fraud Detection (Real Time)
IA / ML / DL models
Feedback from
Case Management
Multi-channel data
Geolocation
Anti-trojan Analysis
Non-Monetary Transaction
Monetary Transaction
TODO1 Threat
Intelligence Network
IP information
Behavioral Profile
Mobile device
transactions
Device profile

7. © 2022 Todo1 Services, Inc. All rights reserved.
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Why is this problem hard ?
• Severe class imbalance
• Lack of public / shared data
• Data quality (labeling, timeliness, and more…)
• “Auto-correlated” observations
• Changing fraud patterns overt time (concept drift)
• Feature generation can be extremely time consuming
• Real-time (< 100ms) response can be hard to manage
• Sheer volume makes it even more complex
• Down-time is not an option
PROBLEM
SOLUTION
IMPLEMENTATION
Why Neo4j..?

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Why Neo4j?
• Feature Generation Time
• Low Latency
• Resiliency
• High Throughput
• Feature Generation Time
• Concept Drift
Schema
Free
Index
Free
Adjacency
In Memory
Scalable
Cluster

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3.2+ BILLION
Protected Transactions per year
$40+ MILLION
In losses prevented
85% 15%
15+ MILLION
Registered Users
Peaks 15+ MILLION A DAY and 400+ per sec
Operational Footprint

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2.2+ BILLION
RELATIONSHIPS
< 100 ms
RESPONSE TIME
250+ MILLION NODES
Operational Footprint

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Challenges and next steps
• Improve backup and checkpoint processes
• Research further cypher optimization (planning, parameterization, etc.)
• Optimize hardware costs
• Build additional fraud prevention functionalities
• Enhance visualization capabilities

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Acknowledgements
1 2
Hernan Rodriguez,
Edison Ruiz and Juan
Henao for a ton of
brain, sweat and tears
David Richard & Alex
Rivilis for a smooth
sales process and a ton
of sense of humor
3 4
Susan DiFranco and
Rodrigo Haces for their post
implementation support and
infinite patience
CMS team for
continuous vigilance
and dedication to our
operation

13. © 2022 Todo1 Services, Inc. All rights reserved.
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Thank you!
Contact us at
eosuna@todo1.com