1. Low-Latency, Web-scale Fraud
Prevention with Samza and Friends

2. Edi Bice
ebice@ebay.com
Senior Data Scientist at eBay Enterprise leading
R&D efforts in applying machine learning to
fraud prevention and elsewhere.

3. Commerce is getting more convenient, more complex, and so is fraud. To
keep up fraud prevention solutions need to process a lot more data
• Older Data
• Looking back a lot further in time
• Older data is not effective excuse – home for the holidays?
• Wider Data
• Using all available data sources
• How wide can customer name possibly be?
• Richer Data
• Social/unstructured data – people, places, interests
• Connected data – who shipped to whom, where; email, devices, IP addresses
• Faster Data
• Clickstream data – website click patterns

4. Modern Fraud Prevention Architecture Requirements
• Web scale capable (horizontal scaling using commodity hardware)
• handle more actions and data for each user
• handle more users and more volume from each user
• handle more customers of all sizes (lowest processing cost)
• Low latency (milliseconds not hours)
• card present, digital goods, gift cards, store pickup (in-store online shopping!?)
• e-commerce physical goods? – no teleporting yet so speed up what we can
• process customer interactions in real time (personalization, royalty, shopping experience)
• dynamic order process (identification, authentication, tender presentation)
• Fault tolerance
• Commodity hardware is not without faults
• Expect and design for routine failures – more like shift changes, or relay races

5. Preventing fraud is all about detecting abnormal behavior.
Normal behavior is not normal – we are all normal in our
own abnormal special ways.
• Typical customer profiling calculations
• Transaction velocity (#txns_day) and change (#txns_day_1days/#txns_day_10days)
• Amount velocity ($txns_day) and change ($txns_day_1days/$txns_day_10days)
• Typical implementation and technologies
1. Define sliding window interval (7 days, a month, 6 months?)
2. For each live txn pull matching txns (card, ...) from single SQL DB within that sliding window
3. Loop over pulled transactions filtering based on timestamp to calculate change over sub-windows
• Issues, Problems, Solutions?!

7. CName Date $ ShipAddr … CTxns CustAvgAmt TxAmt_AvgAmt_Ratio Shipping
Addr Txns
Shipping Addr
Avg Amount
Edi Bice 8/3/15 50 123 Main St 1 50 = (50 + 0) / 1 NA 1 50
Edi Bice 8/3/15 100 123 Main St 2 75 = (100 + 50*1) / 2 2.0 = 100 / 50 2 75
Edi Bice 8/4/15 150 123 Main St 3 100 = (150 + 75*2) / 3 2.0 = 150 / 75 3 100
Edi Bice 8/5/15 1500 999 Wall St 4 450 = (1500 + 100*3) / 4 15.0 = 1500 / 100 1 1500
Streaming Analytics New Avg Amt = (New Txn Amt + Curr Avg Amt
* Curr Num Txns) / (Curr Num Txns + 1)

8. job pipelines
Kafka, Samza, and the Unix philosophy of distributed data by Martin Kleppmann

9. Apache Kafka
• Distributed, scalable, publish-subscribe messaging system
• Persistent, high-throughput messaging
• Designed for real time activity stream data processing

10. PreCog Samza Job Pipeline
Manifold (1-in-N-out) jobs
Risk-by-Y calc jobs
X-by-Y calc jobs
Assembly jobs

12. FAULT-TOLERANT
LOCAL STATE
Samza job partition 0 Samza job partition 1
Local
RocksDB
Local
RocksDB
Durable changelog Kafka
replicate writes
Embedded key-value: very fast
Machine dies ⇒ local key-value store is lost
Solution: replicate all writes to Kafka!
Machine dies ⇒ restart on another machine
Restore key-value store from changelog
Changelog compaction in the background

13. Samza Jobs on Hadoop 2.0 (YARN)
Samza App Master
Node Manager
Kafka Broker
Machine 1 Machine 2
Samza TaskRunner: Partition 1
Node Manager
Kafka Broker
aStreamTask:process()
Samza TaskRunner: Partition 2
aStreamTask:process()
Machine 3
Node Manager
Kafka Broker
Samza TaskRunner: Partition 3
aStreamTask:process()

14. Monitoring Samza: Metrics and More
Samza JMX metrics jmxtrans OpenTSDB/HBase Grafana

15. Questions?
http://www.ebayenterprise.com/
ebice@ebay.com
@edi_bice
https://www.linkedin.com/in/ebice