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How AI is preventing
account fraud at web
scale
Amir Moghimi
Co-founder & CTO
crossclassify.com
1
Byron Bay data breach victim told to pay
Adidas, National Basketball Association
$1.2m by US courts
"The charges were cybersquatting,
trademark infringement, IP infringement,
things I don't know anything about."
ABC North Coast /
25 July 2023
2
3
■ In a survey run by AIC, 47% of respondents in 2023, experienced
at least one cybercrime in the 12 months prior to the survey.
■ 20% of these cybercrimes was identity crime and misuse. *
■ No surprise with Optus, Latitude and Medibank data breaches.
* Australian Institute of Criminology
Running an
online service
User
interactions
Sign-up
Sign-in
Range of fraud
patterns
Accelerate
user acquisition and
forget about fraud
4
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
5
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
6
Detecting
important entities
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
Account Device IP
7
Profile
Construction
Detecting
important entities
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
8
Account Device IP
More than 65 features for Account
9
More than 70 features for Device
10
More than 40 features for IP
11
12
Profile
Construction
Detecting
important entities
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
Account Device IP
Deviation from
normal behavior
13
Profile
Construction
Detecting
important entities
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
Account Device IP
14
Deviation from
normal behavior
Profile
Construction
Detecting
important entities
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
Account Device IP
Statistical Supervised Unsupervised
Supervised
Z-score
Box
whisker
Clustering
Collective
Outliers
Outlier
Score
15
Deviation from
normal behavior
Profile
Construction
Detecting
important entities
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
Account Device IP
Statistical Supervised Unsupervised
16
Supervised
Z-score
Box
whisker
Clustering
Collective
Outliers
Outlier
Score
Deviation from
normal behavior
Profile
Construction
Detecting
important entities
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
Account Device IP
Statistical Supervised Unsupervised
Statistical
Box-Whisker
Outlier: An
account with
9 distinct
devices
Z-Score
Outlier
17
Device Count
Unsupervised
Account sharing like Netflix,
Spotify, Gaming apps
One account with more
than 20 distinct devices and
10 different IPs (Far from
normal accounts)
Accounts with
normal behavior
One account with
approximately near normal
behavior
18
Dev Count
IP Count
Supervised
Outlier
Residual= Anomaly Score
19
20
Supervised
Z-score
Box
whisker
Clustering
Collective
Outliers
Outlier
Score
Deviation from
normal behavior
Profile
Construction
Detecting
important entities
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
Account Device IP
Statistics Supervised Unsupervised
Quantitative
21
Supervised
Z-score
Box
whisker
Clustering
Collective
Outliers
Outlier
Score
Deviation from
normal behavior
Profile
Construction
Detecting
important entities
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
Account Device IP
Statistics Supervised Unsupervised
Qualitative
Precision
Recall F-measure
Adaptability
Explainability Scalability
22
Quantitative
Supervised
Z-score
Box
whisker
Clustering
Collective
Outliers
Outlier
Score
Deviation from
normal behavior
Profile
Construction
Detecting
important entities
AI-powered fraud detection and
prevention system
Understanding
Online Accounts
Entity Description
Fraud Detection
Methods
Evaluation
Account Device IP
Statistics Supervised Unsupervised
Qualitative
23
Explainability-Accuracy Trade-Off
Prediction
Accuracy
Explainability
Learning Techniques (today) Explainability
(notional)
Neural
Nets
Statistica
l
Models
Ensembl
e
Methods
Decisio
n
Trees
Deep
Learnin
g
SVM
s
AOG
s
Bayesian
Belief
Nets
Markov
Models
HBN
s
MLN
s
New
Approach
Create a suite of
machine learning
techniques that
produce more
explainable models,
while maintaining a
high level of
learning
performance
SR
L
CRF
s
Rando
m
Forests
Graphic
al
Models
Challenges
Imbalanced
Dataset
Fraud Patterns
Change/Evolve
Cat & Mouse
Model Selection
+
Parameter Tuning
24
25
Challenges
Imbalanced
Dataset
Fraud Patterns
Change/Evolve
Cat & Mouse
Model Selection
+
Parameter Tuning
Make it
balanced
26
Challenges
Imbalanced
Dataset
Fraud Patterns
Change/Evolve
Cat & Mouse
Model Selection
+
Parameter Tuning
Imbalanced Dataset
Make it balanced
Data
Generation
Data
Augmentation
Sampling
27
Over
Sampling
Under
Sampling
0.001% of
actions are
fraud
Make it
balanced
Ensemble
methods
28
Make it
balanced
Challenges
Imbalanced
Dataset
Fraud Patterns
Change/Evolve
Cat & Mouse
Model Selection
+
Parameter Tuning
Ensemble methods
Model
1
Model
2
Model
3
New
Sample
Final
Prediction
What is the best
combination of
methods?
Random Forest and
XGBoost are best
practices
29
Imbalanced Dataset
Make it
balanced
Ensemble
methods
Adaptive AI
30
Challenges
Imbalanced
Dataset
Fraud Patterns
Change/Evolve
Cat & Mouse
Model Selection
+
Parameter Tuning
Unsupervised methods adaptively
detect fraud with unseen patterns
stream
Dynamic Clustering
Adaptive AI
31
New Inlier
Cluster
Unsupervised methods adaptively
detect fraud with unseen patterns
stream
32
outlier
Dynamic Clustering
Adaptive AI
stream
33
outlier
Unsupervised methods adaptively
detect fraud with unseen patterns
Dynamic Clustering
Adaptive AI
stream
34
Changing the
outlier Cluster
to inlier one
Unsupervised methods adaptively
detect fraud with unseen patterns
Dynamic Clustering
Adaptive AI
Make it
balanced
Ensemble
methods
Adaptive AI Trial and Error
Proper
Modeling
35
Challenges
Imbalanced
Dataset
Fraud Patterns
Change/Evolve
Cat & Mouse
Model Selection
+
Parameter Tuning
Trial and Error /
Experience
At the beginning,
interpretability &
explainability are more
important
As we get more data, more
complicated methods with
large parameters come into
the picture
Model Selection
Parameter Tuning
Implement the
Proper Modeling
36
Account Take Over
Modeling Example
37
Dataset (Account Take Over)
Account Login features:
✔ Timestamp, IP address, Country, City, User agent , … .
✔ Derived features from existing ones
Original dataset consists of:
✔ 34.1 million login attempts (records)
✔ 3.2 million users
✔ 14 features
✔ Non-fraud records: 32269123
✔ Fraud records: 181
38
Challenges
39
Imbalanced Dataset
Parameter Tuning
Explainability
Under Sampling
Neural Network Modeling
Decision Tree
Dataset sampling
✔ Non-fraud records: 32,269,123
✔ Fraud records: 181
✔ Too imbalanced for neural network classifiers
✔ Train and test split: 20% test size
✔ Under sampling for showcasing different model types:
• Under sampled the non-fraud records to 1,000
40
Layers architecture Parameter No. Recall F1
1 13 – 64 – 1 961 4% 6%
2 13 – 128 – 1 1,921 4% 7%
3 13 – 256 – 128 – 1 36,609 11% 19%
4 13 – 128 – 256 – 256 – 128 – 1 133,633 71% 23%
5 13 – 128 – 512 – 256 – 128 – 1 232,193 14% 20%
Neural Network
41
Decision Tree
42
43
Questions?
44

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