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Data Warehouse Structures for AML Applications J ERZY K ORCZAK, Wroclaw University of Economics LSIIT, CNRS, Strasbourg, France LSIIT, CNRS, Strasbourg,

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Presentation on theme: "Data Warehouse Structures for AML Applications J ERZY K ORCZAK, Wroclaw University of Economics LSIIT, CNRS, Strasbourg, France LSIIT, CNRS, Strasbourg,"— Presentation transcript:

1 Data Warehouse Structures for AML Applications J ERZY K ORCZAK, Wroclaw University of Economics LSIIT, CNRS, Strasbourg, France LSIIT, CNRS, Strasbourg, France B ŁAŻEJ O LESZKIEWICZ, Wroclaw, Poland 1

2 Money Laundering - Definition Money laundering is the practice of engaging in financial transactions in order to conceal the identity, source, and/or destination of money, and is a main operation of the underground economy. In this paper: identification the methods and technology of the anti- money laundering (AML) process introduction of SART system structures of data warehouse selected problems of AML systems 2/40

3 Outline Problem of AML – the state of the art Fundamental aspects of AML system design System for Analysis and Registration of Transactions Architecture of data warehouse Case study – examples of a few selected problems Conclusion and future research 3

4 Process of money laundering Stages: Placement: refers to the initial point of entry for funds derived from criminal activities. Layering: refers to the creation of complex networks of transactions which attempt to obscure the link between the initial entry point, and the end of the laundering cycle. Integration: refers to the return of funds to the legitimate economy for later extraction. 4

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6 Examples of stages of the process Placement StageLayering StageIntegration Stage Cash paid into bank (sometimes with staff complicity or mixed with proceeds of legitimate business). Wire transfers abroad (often using shell companies or funds disguised as proceeds of legitimate business). Resale of goods/assets. Income from property or legitimate business assets appears "clean". Monies are placed into retail economy or are smuggled out of the country Complex web of transfers (both domestic and international) makes tracing original source of funds virtually impossible. Establishment of anonymous companies Transformation into other asset forms: travellers cheques,postal orders,etc. Cash exported. Cash deposited in overseas banking system. Sending of false export- import invoices overvaluing goods 6

7 AML software – the state-of-the-art packages include capabilities of name analysis, rules- based systems, statistical and profiling engines, neural networks, link analysis, peer group analysis, and time sequence matching KYC solutions that offer case-based account documentation acceptance and rectification, as well as automatic risk scoring of the customer (taking account of country, business, entity, product, transaction risks) other elements: portals to share knowledge and e- learning for training and awareness 7

8 Types of AML systems All financial institutions globally are required to monitor, investigate and report transactions of a suspicious nature to the financial intelligence unit of the central bank in the respective country. Types of software addressing AML business requirements: Currency Transaction Reporting (CTR) systems, which deal with large cash transaction reporting requirements (15,000 E) Customer Identity Management systems which check various negative lists (such as OFAC) and represent an initial and ongoing part of Know Your Customer (KYC) requirements Transaction Monitoring Systems, which focus on identification of suspicious patterns of transactions which may result in the filing of Suspicious Activity Reports (SARs). Identification of suspicious (as opposed to normal) transactions is part of the KYC requirements. 8

9 Modules of AML system Software applications effectively monitor bank customer transactions on a daily basis and, using customer historical information and account profile, provide a "whole picture" to the bank management. Each vendor's software works somewhat differently; some of the modules in an AML software are: Know Your Customer (KYC) Entity Resolution Transaction Monitoring Compliance Reporting Investigation Tools 9

10 Transaction Monitoring Systems TMS focus on identification of suspicious patterns of transactions which may result in the filing of Suspicious Activity Reports (SARs). Identification of suspicious transactions is part of the KYC requirements. Financial institutions face penalties for failing to properly file CTR and SAR reports, including heavy fines and regulatory restrictions, even to the point of charter revocation. 10

11 Outline Problem of AML – the state of the art Fundamental aspects of AML system design System for Analysis and Registration of Transactions Architecture of data warehouse Case study – examples of a few selected problems Conclusion and future research 11

12 Typical solutions vs. Analytical SQL Server Typical solutions vs. Analytical SQL Server 12

13 Architecture of Analytical SQL Server 13

14 SART internal architecture 14

15 Major modules of SART 15

16 Major modules of SART 16

17 Outline Problem of AML – the state of the art Fundamental aspects of AML system design System for Analysis and Registration of Transactions Architecture of data warehouse Case study – examples of a few selected problems Conclusion and future research 17

18 Main issues Problem of scalability Data structure Charts of Accounts of General Ledger OLAP Data Warehouse based on General Ledger Reporting Transaction chains 18

19 19 Architecture of data warehouse Problem of scalability Size of dimensions: General Ledger Dimension 60 000 entries, Bank customers Dimension 500 000 entries, Time Dimension 3600 entries (the duration of operations 10 years), Number of measures in OLAP cube is 5.

20 20 Architecture of data warehouse Problem of scalability Approximate size of OLAP cube of 230.2 PB Approximate calculation of the indicated OLAP cubes size shows that it is not feasible to store OLAP data without Compression. Approximate number of entries in OLAP cube was: 60 000 ×500 000 × 3600 × 5 = 0.54*10 15. Considering the minimum size of data stored in OLAP cube (4 bytes dimension identifier, 8 bytes measures value) this value should increase by 3×4×5×8 = 480 times that is 259.2*10 15 bytes

21 21 Heterogeneous data warehouse dimensions of General Ledger

22 22 Homogenous dimensions of TIME

23 Data Warehouse of General Ledger Modeling and implementation of the Data Warehouse of General Ledger Fact Table Dimensions 23

24 Data Warehouse of General Ledger Star Schema 24

25 Data Warehouse of General Ledger Normalized Time Dimension in a Snowflake Schema 25

26 Data Warehouse of General Ledger Hierarchical schema (always a la star schema) 26

27 Data Warehouse – Fact Table 27

28 Facts Table – operation 28

29 Facts Table – operation 29

30 Facts Table – operation 30

31 Facts Table – operation 31

32 Facts Table and Charts of Account 32

33 33 Integration of accounting model and transaction mode

34 Summary of data structure Technological characteristics: non uniform hierarchy number of nodes: 61 297 within number of synthetic accounts: 29 268 max depth: 10 Application characteristics: decrees dictionary of General Ledger dictionary of transaction accounts dimensions of data warehouse 34

35 Outline Problem of AML – the state of the art Fundamental aspects of AML system design System for Analysis and Registration of Transactions Architecture of data warehouse Case study – examples of a few selected problems Conclusion and future research 35

36 Case Study – some statistics sample database number of processed records (daily): min: ~1,000 rec. (weekend) max: ~300,000 rec. (end of month) monthly (January 2008) total: 2,497,280 rec./month daily average : 80,557 rec. DW dimensions: 197,046 rec. 36

37 Characteristics of DW (General Ledger) 13,299,773 rows in Facts Table 20,581,733 Cartesian products in OLAP Cube 970,987,198 number of OLAP operations executed during recomputing of OLAP cube 970.987.198 / 20.581.733 = 47,177 average number of OLAP operations over registered decree 37

38 OLAP Data Warehouse of General Ledger Implementation of the OLAP DW of General Ledger Fact Table Dimensions OLAP Cube Cube Pivot as changes viewing in OLAP cube 38

39 OLAP Data Warehouse – General Ledger 39

40 OLAP Data Warehouse – General Ledger 40

41 OLAP Data Warehouse – OLAP Raport with view on the Charts of Account dimension 41

42 OLAP Data Warehouse – OLAP Raport with view on the Charts of Account dimension 42

43 Data Warehouse – Cube Pivot 43 OLAP operation Dimension General Ledger Dimension Client Dimension General Ledger Dimen. Time Dimension Time Dimension Client Cash in ZLP Account 1 Account N

44 OLAP Data Warehouse – OLAP Raport with view on the Charts of Account dimension 44

45 OLAP Data Warehouse – OLAP Raport with view on the Charts of Account dimension 45

46 OLAP Data Warehouse – General Ledger 46

47 Performance of DW SART OLAP Data import: 57,952 decrees OLAP recalculation: OLAP calculation: 4 min. 10 sec (250 sec.) OLAP operations: 5,275,770 number of created Cartesian products OLAP: 991,675 average number of OLAP operations/sec: 21,103.1 op./sec. 47

48 Performance of DW SART OLAP OLAP reporting performance: Chart of Accounts dimension OLAP view: Maximum: ~2 sec. Maximum: ~2 sec. Average: ~0.6 sec. Average: ~0.6 sec. Time dimension OLAP view: Maximum: ~1.2 sec. Maximum: ~1.2 sec. Average: ~0.4 sec. Average: ~0.4 sec. 48

49 Summary of DW architecture the SART OLAP Data Warehouse model based on non-uniform dimensions OLAP model based on non-uniform dimensions Cube Pivot operation with slice functionality 49

50 SART – Transaction merging Transaction merging process in SART Building a transaction model based on the General Ledger decrees Integration of the transaction model with the General Ledger accounting model Integration of the transaction model with a OLAP reporting 50

51 SART – Transaction merging 51

52 SART – Transaction merging 52

53 SART – Transaction merging 53

54 SART - Cash Flow Chains Analysis Cash Flow Chains Analysis (CFCA) Cash Flow Chains OLAP Data Warehouse of Cash Flow Chains Cash Flow Chains Analysis – example of use 54

55 SART - Cash Flow Chains Analysis 55

56 Cash Flow Chains Analysis 56

57 Transaction chains – trees view 57

58 Transaction chains – trees view 58

59 Transaction chains (cash flow) 59

60 SART - Cash Flow Chains Analysis Four major CFCA rates Source Accounts/Transaction chains ratio Destination Accounts/Transaction chains ratio Inner Accounts/Transaction chains ratio Number of account cycle chains 60

61 CASE STUDY SART - Cash Flow Chains Analysis Sample database of OLAP Data Warehouse CFCA Number of transactions : 46,459 Number of accounts in CFCA: 38,844 Number of chains: 5,021,459 Number of chains links: 29,567,581 61

62 CASE STUDY Analysis of Transaction Chains In the case study it will be analyzed transaction chain from the source account (id= 22921 ) to the target account (id= 14037).

63 CASE STUDY Transaction Chain Analysis Report characteristics: # of generated chains 674; # of generated chains 674; # of transactions participating in chains 88; # of transactions participating in chains 88; # of source accounts of sub-chains 5; # of source accounts of sub-chains 5; # of target accounts of sub-chains 5; # of target accounts of sub-chains 5; Important risk of ML using shell companies.

64 Cash Flow Chains Analysis Wybrane konto źródłowe

65 Cash Flow Chains Analysis Wybrane konto źródłowe i docelowe (wynik zapytania)

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73 Conclusions and future works SART has been implemented SART does not need any suppl. components high system performance -> ~ real time extensions: credit analysis, operational risk, … Further research: standardisation of the data warehouse model development of BI and CI mapping Object/Relation model in SART study of data mining algorithms 73


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