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Phone Fraud Detection
1. 1
Phone Fraudsters in a Haystack
Sri Kanajan, Prasad Telekuntla, Mijail Gomez
3rd place in Tata Telecommunications Global Hackathon
2. 2
Leaves International Missed Call
Unknowingly Calls Premium Number or
Manipulative Advertisement
$2 BILLION OF LOST REVENUE FROM
TELCOM PROVIDERS
Example of Phone Fraud
3. 3
Motivations
• Current statistical solutions have low specificity and sensitivity
• Human fraud analysts have to continually update their heuristic
based rules and thresholds
• Need an adaptive solution that works in real time with minimal false
positives
4. 4
Statistical
Analysis
Anomaly
Detection
Live Streaming
Phone Data
Hybrid Statistical and Machine Learning Solution
Number of Callers/Callee/Cumulative Call
Duration
Machine Learning
(Random Forests)
Evaluation of other features in the call log such as
answer indicator, area code, pricing…
Used Hackathon De-identified Phone Log
Dataset 16 GB
5. 5
Anomaly Detection Through Statistical Analysis
# of Unique Caller’s per Phone
Number
# of Unique Callee’s per Phone
Number
Cumulative Duration of Calls to
Specific Phone Numbers
ANOMALOUS Phone Numbers!!
7. 7
Fraud Detection Using Graph Metrics
• Triangle Counting
• PageRank
• Others… Note: Goal is to uncover the callers that are
very different from the large majority
8. 8
Using Principal Component Analysis to uncover the outliers in the graph metrics
Fraud Detection Using Graph Metrics
Possible Fraudsters!
12. 12
Ensemble Model – Machine Learning and Statistical
• With labeled data, the classifier can progressively identify patterns
beyond the graph metrics (uses all other features in the raw call log)
– E.g. patterns in area codes or specific pricing plans used by fraudsters
• Active learning is done online while the system is active. I.e. the
longer the system is in use, the better it gets