The document discusses a three-tiered analytics solution for anti-money laundering (AML) that involves sanitizing and organizing data, analyzing the data through rules and machine learning, and performing post-classification tasks. It describes using JBoss data grid and virtualization to integrate various data sources and analyzing transaction data through MapReduce jobs. Rules are generated from the data and a UI and workflow are used to classify transactions and create tasks for an analyst. The system is designed to be adaptive by updating rules based on insights from historical data and parameter tuning.

1. THE INTERSECTION
OF BUSINESS RULES
MANAGEMENT AND BIG DATA
Anurag Saran, Sal Elrahal, Murph
Red Hat
June 30, 2016
#redhat #rhsummit
1

2. AGENDA
BECAUSE AGEN DAS ARE AWESOME
INTRODUCTIONS
BACKGROUND: AML
THREE TIERED ANALYTICS SOLUTION
CUSTOMER SUCCESS STORY
Q&A
1
2
3
Sanitize and Organize the Data
Analyze the Data
Post Classification Tasks
2

3. BACKGROUND: AML
#redhat #rhsummit
3.1

4. AML is practice of integrating the proceeds of crime into the legitimate
mainstream of the financial community by concealing its origin.
Team Good Guy:
Financial institutions,
Regulators and
law enforcement
In 1920;s, Gangsters had money in small denominations. They started
business of slot machines and laundromats. Thus the term laundering.
AML AND HISTORY
3.2

5. REGUL ATO RY
EN VIR ONMENT
OFFICE OF
FO REI GN AS SETS
CO NTROL (OFAC)
3.3

6. PILL ARS OF AML
3.4

7. KEY INGREDIENTS
3.5

8. WEB BASED AUTHORING – PROCESS
3.6

9. CONTINUED – DECISION TABLE
3.7

10. DATA
JB OSS DATA GRID & DATA V IR TU AL I Z ATION
JB OSS DATA GRID
In-Memory Distributed Caching
Distributed Caching and Compute
Embedded or Service
Transactional and Persistent
Peer-to-Peer Linear Scaling
JB OSS DATA
VI RT UAL I Z AT I O N
Provide Virtual Data Layer
Consume Databases, Services, Files,
NoSQL
Expose JDBC, ODBC, REST, SOAP, ODATA
Transactions, Caching, Advanced Joins
3.8

11. THREE TIERED SOLUTION
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4 .1

12. REFERENCE ARCHITE CTURE
4 .2

13. BUILD AN ADAPTIVE SYSTEM
4 .3

14. USE RULE S IN MAP AND REDUCE. USE
PIG SCRIP T TO GENER ATE RULE S
4 .4

15. BUILD SM ARTER RULE S USING STATIC,
LINEA R REGRE SSION AND SOCI AL
GRAPH INPUT S
4 .5

16. BUILD A UI THAT YOUR
USERS WILL LOVE
4 .6

17. 4 .7

18. BUILD COMP ONENT BASED L AYOUT
4 .8

19. SANITIZE AND ORGANIZE THE DATA
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5.1

20. 5.2

21. KNOW YOUR CUSTOMER-USE FREE DATA
5.3

22. ANALYZE THE DATA
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6.1

23. HAN D WRITTEN RULES I N MAP REDUCE
GENERATED RULE S FR OM DATA
ANA LYSIS
TRANSACTION ENGINE USAGE
6.2

24. HAN D WRI TTEN RUL ES I N MAP REDUCE
MapReduce with Transaction Profile
Java Code
Rules
6.3

25. TRANSACTION PROFILE
CC # Amount State Profession
1234123412341234 $105.67 NC Gas Station
5678567856785678 $33.97 MD Ice Cream Parlor
1234123412341234 $822.97 NC Gas Station
1029102910291029 $610.20 TX Artist
..... ...... ..... .....
State Profession Avg Std Dev ....
NC Gas Station $321.23 $22.13 .....
NC Writer $22.67 $59.08 .....
MD Ice Cream Parlor $523.73 $30.23 .....
TX Artist $987.42 $303.76 .....
..... ...... ..... ..... .....
MAP REDUCE
6.4

26. public void map(Object lineNumber, Text line, Context output) {
Transaction transaction = new Transaction(value.toString());
String state = transaction.getState();
String profession = transaction.getProfession();
String key = state + “-” + profession;
output.write(new Text(key), new Text(transaction.toString()));
}
public void reduce(Text key, Iterable<Text> values, Context output) {
TransactionProfile transactionProfile = new TransactionProfile();
for(Text value: values) {
Transaction transaction = new Transaction(value.toString());
addTransaction(transactionProfile, transaction);
}
output.write(key, new Text(transactionProfile.toString()));
}
public void addTransaction(TransactionProfile transactionProfile, Transaction transaction) {
transactionProfile.updateTotal(transaction.getAmount());
}
MAP PHASE
REDUCE
PHASE
6.5

27. when
$transaction : Transaction()
then
mapTo($transaction.state + $transaction.profession, $transaction)
end
when
$transaction : Transaction()
$transactionProfile : TransactionProfile()
then
$transactionProfile.updateTotal($transaction.amount)
end
MAP PHASE
REDUCE
PHASE
6.6

28. GENERATED RULES FROM BIG DATA
Joining Data Types with Pig
Linear Regression
6.7

29. JOINING DATA TYPE S
A# Name State City Zip Age Sex Type ...
5532 Sal NC Charlotte 28206 65 M Chk ...
2341 Murph NY New York 24439 82 M Sav ...
7424 Anurag NJ Edison 29945 81 M Sav ...
1643 Derik MD Bowie 44293 75 M Bus ...
T# A# Amount Type From Zip ToZip IP Country ...
00004213 7424 NC CC 28206 28202 96.12.23.3 US ...
00000213 2341 NY DR 24439 28826 8.7.6.5 US ...
00053322 7424 NJ CC 29945 34435 1.2.3.4 US ...
00008843 2341 MD MT 44293 29846 34.34.34.2 US ...
ACCOUNT DATA
TRANSACTION DATA
6.8

30. transaction = load '/user/aml-demo/trans.txt/' using PigStorage(',') as …..
account = load '/user/aml-demo/account.txt' using PigStorage(',') as …..
C = foreach account generate AccountNo as id, ZipCode,Occupation;
jnd = join transaction by AccountNo, C by id;
D = group jnd by (C::ZipCode,transaction::TransactionType,C::Occupation);
E = foreach D generate flatten(group) as (zip,Tranaction,occupation),SUM($1.Amount) as tota
STORE E into 'idout' USING PigStorage(',');
6.9

31. TR ANS AC TION ENGINE USAGE
Actionable - Leverage all generated rules
Adaptive - Historically & Parameter Tuning
6.10

32. AC TIONABL E
6.11

33. ADAP TIVE - HISTO RICAL LY
6.12

34. ADAP TIVE - PAR AM ETER TUNING
6.13

35. POST CLASSIFICATION TASKS
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7.1

36. BX MS DESI GN PAT TERNS
7.2

37. MODEL
DOCUMENT BASED GENERATION
GUIDED EDITING ...
7.3

38. GUIDED EDITING

39. DYNAMI C SUB PROCESSES
7.5

40. VE RS I ON, BUI L D, STO RE, DEPLOY
7.6

41. VE RS I ON, BUI L D, STO RE, DEPLOY
7.7

42. package example;
import org.kie.api.runtime.KieSession;
import org.kie.api.runtime.manager.RuntimeEngine;
import org.kie.remote.client.api.RemoteRestRuntimeEngineBuilder;
import org.kie.remote.client.api.RemoteRuntimeEngineFactory;
public class Client {
public static void main(String[] args) {
RemoteRestRuntimeEngineBuilder builder = RemoteRuntimeEngineFactory.newRestBuilder()
.addDeploymentId(deployment)
.addUrl(url)
.addUserName(username)
.addPassword(password)
.addTimeout(timeout);
RuntimeEngine runtimeEngine = builder.build();
KieSession session = runtimeEngine.getKieSession();
session.startProcess(process, parameters);
}
}
EXECUTE
7.8

43. EXECUTE
7.9

44. var url = util.getKieServerUrl()
+ "/kie-server/services/rest/server/queries/tasks/instances/pot-owners"
+ "?page=" + page;
$http.defaults.headers.common.Authorization = 'Bearer ' + $scope.token;
$http.defaults.headers.common['Accept'] = "application/json";
$http.get(url)
.success(function (data) {
$scope.data.result = data['task-summary'];
})
.error(function (error) {
$scope.data.error = {};
$scope.data.error.code = 'getMyTasks';
$scope.data.error.message = 'The task list could not be retrieved.'
});
EXECUTE!
7.10

45. EXECUTE
7.11

46. SUMMARY
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8.1

47. SANITIZE AND ORGANIZE THE DATA
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8.2

48. ANALYZE THE DATA
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8.3

49. POST CLASSIFICATION TASKS
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8.4

50. System Architecture – Transaction/BIG Data Phase
Data Grid
JBOSS EAP
BPMS
Happy Path
New Transaction
Check Rules
No Rules Applied
Pass Through Transactions

51. System Architecture – Transaction Phase
Data Grid
JBOSS EAP
BPMS
Suspicious Path
New Transaction
Apply Rules
Score incoming transaction
Task Created for analyst to work On.
Retrieve Historical Trasaction and store
in cache for analysis.

52. System Architecture – Insight Phase
Data Grid
JBOSS EAP
BPMS
Fraud Transaction
Customer Insight Rules Insight
Rule Insight – Know exact rules that
were applied.
Data Insight - Slice/Dice data

53. System Architecture – Task Phase
Data Grid
JBOSS EAP
BPMS
Fraud Transaction
Customer Insight Rules Insight
Work On Task.
Invoke relevant sub process

54. System Architecture – Update Phase
Data Grid
JBOSS EAP
BPMS
Customer Insight Rules Insight
With transaction insights,
modify/update rules.
With rules insights, modify/update
rules.
Run PIG jobs to update rules

55. CUSTOMER SUCCESS
STORY
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9.1

56. 9.2

57. 9.3

58. 9.4

59. 9.5

60. 9.6

61. 9.7

62. 9.8

63. 9.9

64. 9.10

65. QUESTIONS
?
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66. LEARN. NETWORK.
EXPERIENCE OPEN
SOURCE.
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