Finam is the leader in the brokerage sector in Russia, and they enable investors to work with national and international stock exchanges. This business has high demands in client security and compliance, and fraud prevention. A CEP framework was chosen as the best solution for these demands, and WSO2 CEP was selected for a pilot solution. The story is useful for organizations that want to get started with WSO2 CEP and specifically looking for a business tasks solution with low-cost implementation.

1. Fraud Prevention and Compliance in Financial
Sector with WSO2 CEP Solution
Pavel Litvinov

2. About Company
2
• The national leader and global broker in the field of investment
services
• The group’s companies operate in Russia, the USA, the EU, India,
China, Thailand, Indonesia, Great Britain and other countries
• More than 319 000 clients in 40 countries worldwide
• The highest reliability rating: A++
• Financial instruments market turnover $1.2 trillion

3. About Company
3
Clients Broker Stock Exchanges
RegulatorsFrauds

4. Fraud
4
Unusual orders:
o Volume
o Instruments
o Frequency
Operations
• Multiple Connections
from single IP
• Unusual device
• Time and Location
Access
Combination
Frauds

5. Regulator Rules
5
Regulators
Market
manipulation
Insider trading
• spoofing
• pump & dump
• front running
• layering

6. Specify
6
market data
Access alerts
Thief alerts
Compliance
Exchanges
Other
Trade
platforms
orders/trades
orders / access
news
risk info
Alert
consumers
up to 25.000 deals per second
up to 1.0 M stock data changes per second

7. Why CEP
7
Why CEP:
✓ Real time
✓ Work with many streams
✓ Fast implement
✓ Minimize coding
Why
✓ Open Source / Free
✓ Tests
✓ In build language - Siddhi
✓ Integration in out infrastructure

8. Integration
8
Market data
CEP
Exchanges Gates
gRPC
JMS
Converter thrift

9. Integration
9
Access data
CEP
Trade
System1
JMS
GeoIP
Trade
System2
Trade
System2
ESB
Backoffice,
etc.

10. Integration
10
CEP
Database
BPM
Anti-fraud
portal
CRM
JMS ESB

11. Analyze
11
event
CEP
event
eventevent
eventevent
Transform Detect
Correlate
Aggregate
Filter
event
event
eventevent
ESB

12. Analyze. Siddhi
12
Aggregate events
window
▪ time intervals
▪ count
▪ uniqueness
Filter
Correlation events
SQL-like queries
▪ Input & Output are streams
Easy to learn

13. Learning
13
Pilot project
3 days
3 days
5 days
JMS
grpc
Analyze
Total:

14. Use cases
14
Market manipulation - spoofing
1. Place a first small order
2. Place a technical big order in opposite direction to first one
3. Wait for execution of first order
• Milliseconds time window
• Group by account and security
4. Remove technical order

15. Use cases
15
Access thief alert
Long distance and short time
• Internet bank access
• Trade terminal access
• Bank card usage location

16. Use cases
16
Account thief alert
16
Unusual orders:
• Volume
• Instruments
• Frequency
Operations
• Unusual device
• Time and Location
Access
Combination

17. Use cases
17
Trading signals
Company A price changed by 2% in last 2 min
10 referrers of the company in last 20 min
Stock market
News

18. Use cases
18
News analyze
referrers of our company
negative words
5 times in an hour Marketing

19. Conclusion
19
Fast implement
Improve using of existed data
New perspectives and business possibilities
BPMESBCEP ML

20. Thanks