data warehouse structures for aml applications j erzy k orczak, wroclaw university of economics...
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Data Warehouse Structures Data Warehouse Structures for AML Applicationsfor AML Applications
JJERZYERZY K KORCZAKORCZAK,, Wroclaw University ofWroclaw University of EconomicsEconomics LSIIT, CNRS, Strasbourg, FranceLSIIT, CNRS, Strasbourg, France
BBŁAŻEJŁAŻEJ O OLESZKIEWICZLESZKIEWICZ, Wroclaw, Poland, Wroclaw, Poland
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Money Laundering - DefinitionMoney Laundering - Definition
Money launderingMoney laundering is the practice of engaging in financial is the practice of engaging in financial transactions in order to conceal the identity, source, and/or transactions in order to conceal the identity, source, and/or destination of money, and is a main operation of the destination of money, and is a main operation of the underground economy.underground economy.
In this paper:In this paper:
iidentifdentificationication the methods the methods and technology and technology of the of the anti-anti-
money money llaundering aundering (AML) (AML) process process
introduction of SART systemintroduction of SART system
structures of data warehousestructures of data warehouse
selected problems of AML systemsselected problems of AML systems
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OutlineOutline
Problem of AML – the state of the artProblem of AML – the state of the art
Fundamental aspects of AML system designFundamental aspects of AML system design
System for Analysis and Registration of TransactionsSystem for Analysis and Registration of Transactions
Architecture of data warehouseArchitecture of data warehouse
Case study – examples of a few selected problemsCase study – examples of a few selected problems
Conclusion and future researchConclusion and future research
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Process of money launderingProcess of money laundering
SStagestages::
PlacementPlacement:: refers to the initial point of entry for refers to the initial point of entry for funds derived from criminal activities.funds derived from criminal activities.
LayeringLayering:: refers to the creation of complex networks refers to the creation of complex networks of transactions which attempt to obscure the link of transactions which attempt to obscure the link between the initial entry point, and the end of the between the initial entry point, and the end of the laundering cycle.laundering cycle.
IntegrationIntegration:: refers to the return of funds to the refers to the return of funds to the legitimate economy for later extraction.legitimate economy for later extraction.
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Examples of stages of the processExamples of stages of the process
Placement Stage Layering Stage Integration 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
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AML software – the state-of-the-artAML software – the state-of-the-art
packages include capabilities of name analysis, rules-packages include capabilities of name analysis, rules-based systems, statistical and profiling engines, neural based systems, statistical and profiling engines, neural networks, link analysis, peer group analysis, and time networks, link analysis, peer group analysis, and time sequence matchingsequence matching
KYC solutions that offer case-based account KYC solutions that offer case-based account documentation acceptance and rectification, as well as documentation acceptance and rectification, as well as automatic risk scoring of the customer (taking account automatic risk scoring of the customer (taking account of country, business, entity, product, transaction risks)of country, business, entity, product, transaction risks)
oother elementsther elements: : portals to share knowledge and portals to share knowledge and e-e- learning for training and awareness learning for training and awareness
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Types of AML systemsTypes of AML systems
AAll financial institutions globally are required to monitor, investigate and ll financial institutions globally are required to monitor, investigate and report transactions of a suspicious nature to the financial intelligence unit of report transactions of a suspicious nature to the financial intelligence unit of the central bank in the respective country.the central bank in the respective country.
TTypes of software addressing AML business requirements:ypes of software addressing AML business requirements:
Currency Transaction Reporting Currency Transaction Reporting (CTR) systems, which deal with large (CTR) systems, which deal with large
cash transaction reporting requirements (1cash transaction reporting requirements (155,000 ,000 EE))
Customer Customer IIdentity dentity MManagement anagement systems which check various negative systems which check various negative
lists (such as OFAC) and represent an initial and ongoing part of Know lists (such as OFAC) and represent an initial and ongoing part of Know
YYour our CCustomer (KYC) requirementsustomer (KYC) requirements
Transaction Transaction MMonitoring onitoring SSystemsystems, which focus on identification of , which focus on identification of
suspicious patterns of transactions which may result in the filing of suspicious patterns of transactions which may result in the filing of
Suspicious Activity Reports (SARs). Identification of suspicious (as Suspicious Activity Reports (SARs). Identification of suspicious (as
opposed to normal) transactions is part of the KYC requirements.opposed to normal) transactions is part of the KYC requirements.
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Modules of AML systemModules of AML system
SSoftware applications effectively monitor bank customer oftware applications effectively monitor bank customer transactions on a daily basis and, using customer historical transactions on a daily basis and, using customer historical information and account profile, provide a "whole picture" information and account profile, provide a "whole picture" to the bank management. to the bank management.
Each vendor's software works somewhat differentlyEach vendor's software works somewhat differently; s; some ome of the modules in an AML software are:of the modules in an AML software are:
Know Your CustomerKnow Your Customer (KYC) (KYC)
Entity ResolutionEntity Resolution
Transaction MonitoringTransaction Monitoring
Compliance ReportingCompliance Reporting
Investigation ToolsInvestigation Tools9
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Transaction Monitoring SystemsTransaction Monitoring Systems
TMSTMS focus on identification of suspicious patterns of focus on identification of suspicious patterns of transactions which may result in the filing of Suspicious transactions which may result in the filing of Suspicious Activity Reports (SARs). Identification of suspicious Activity Reports (SARs). Identification of suspicious transactions is part of the KYC requirements.transactions is part of the KYC requirements.
Financial institutions face penalties for failing to properly Financial institutions face penalties for failing to properly file CTR and SAR reports, including heavy fines and file CTR and SAR reports, including heavy fines and regulatory restrictions, even to the point of charter regulatory restrictions, even to the point of charter revocation.revocation.
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OutlineOutline
Problem of AML – the state of the artProblem of AML – the state of the art
Fundamental aspects of AML system designFundamental aspects of AML system design
System for Analysis and Registration of TransactionsSystem for Analysis and Registration of Transactions
Architecture of data warehouseArchitecture of data warehouse
Case study – examples of a few selected problemsCase study – examples of a few selected problems
Conclusion and future researchConclusion and future research
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Typical solutions vs. Analytical SQL ServerTypical solutions vs. Analytical SQL Server
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DBMS SQLServer
OLAPServer
ApplicationServer
Workstation
WorkstationWorkstation
Data integration
AnalyticalServer
Workstation
WorkstationWorkstation
AnalyticalSQL
Server
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Architecture of Analytical SQL ServerArchitecture of Analytical SQL Server
SQL SERVER
SQL Vector Engine
SQL ExtensionBusiness Intelligence
SQL ExtensionAnalytic Intelligence
Fact tables(clasical and vectorized)
Hierarchical Tables (ROLAP Dimensions)
ROLAP Cubes
ROLAP DataMart Cubes (Vectorized ROLAP Objects)
Linear Algebra
Statistics, Econometry
Linear Programming
Additional Analytical Extensions
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SART internal architectureSART internal architecture
ApplicationLayer
SARTApplication
AnalyticalIntelligence
Analysis
BusinessIntelligence
OLAP Data Warehouse
Analytical SQL Server
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Major modules of SARTMajor modules of SART
SART
Personal Data Register
Entity Data Register
Customersand Actual
Beneficiaries Register
Reporting
Data Import/Export Module
Transaction Register
Account Register
Customer Assessment
Customer Behaviour Monitoring
Transactions Register for
GIIF
Bank Transactions
Analysis Module
Risk Analysis
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Major modules of SARTMajor modules of SART
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SART
Reporting
Data Import/Export Module
Transaction Register
Account Register
Customer Behaviour Monitoring
Bank Transactions
Analysis Module
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OutlineOutline
Problem of AML – the state of the artProblem of AML – the state of the art
Fundamental aspects of AML system designFundamental aspects of AML system design
System for Analysis and Registration of TransactionsSystem for Analysis and Registration of Transactions
Architecture of data warehouseArchitecture of data warehouse
Case study – examples of a few selected problemsCase study – examples of a few selected problems
Conclusion and future researchConclusion and future research
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Main issues
Problem of scalability Problem of scalability
Data structure Charts of Accounts of General Data structure Charts of Accounts of General LedgerLedger
OLAP Data Warehouse based on General LedgerOLAP Data Warehouse based on General Ledger
ReportingReporting
Transaction chainsTransaction chains
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Architecture of data warehouseArchitecture of data warehouseProblem of scalabilityProblem 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.
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Architecture of data warehouseArchitecture of data warehouseProblem of scalabilityProblem of scalability
Approximate size of OLAP cube of 230.2 PB
Approximate calculation of the indicated OLAP cube’s 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*1015 .
Considering the minimum size of data stored in OLAP cube (4 bytes dimension identifier, 8 bytes measure’s value) this value should increase by 3×4×5×8 = 480 times that is 259.2*1015 bytes
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Heterogeneous data warehouse dimensions of General Ledger
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Homogenous dimensions of TIME
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Data Warehouse of General LedgerData Warehouse of General Ledger
Modeling and Modeling and iimplementation of the Data mplementation of the Data Warehouse of General LedgerWarehouse of General Ledger
Fact TableFact TableDimensionsDimensions
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Data Warehouse of General LedgerData Warehouse of General Ledger
Star SchemaStar Schema
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fact_table
PK id
FK1 id_time_dimFK3 id_entity_dimFK2 id_chart_of_accounts_dim debits_sum debits_count credits_sum credits_count all_count
time_hierarchy_dim1
PK id
year quarter month day
chart_of_accounts_dimension
PK id
account_name account_type account_code
entities_dimensions
PK id
entity_name entity_type
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Data Warehouse of General LedgerData Warehouse of General Ledger
Normalized Time Dimension in a Snowflake SchemaNormalized Time Dimension in a Snowflake Schema
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fact_table
PK id
FK1 id_time_dimFK3 id_entity_dimFK2 id_chart_of_accounts_dim debits_sum debits_count credits_sum credits_count all_count
time_dim_day
PK id
FK1 id_month day
chart_of_accounts_dimension
PK id
account_name account_type account_code
entities_dimensions
PK id
entity_name entity_type
time_dim_year
PK id
year
time_dim_quarter
PK id
FK1 id_year quarter
time_dim_month
PK id
FK1 id_quarter month
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Data Warehouse of General LedgerData Warehouse of General Ledger
Hierarchical schema (always a la star schema)Hierarchical schema (always a la star schema)
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fact_table
PK id
FK1 id_time_dimFK3 id_entity_dimFK2 id_chart_of_accounts_dim debits_sum debits_count credits_sum credits_count all_count
time_hierarchy_dim
PK id
FK1 id_node node_class node_value
chart_of_accounts_dimension
PK id
account_name account_type account_code
entities_dimensions
PK id
entity_name entity_type
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Data Warehouse – Fact TableData Warehouse – Fact Table
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Facts Table – operationFacts Table – operation
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Facts Table – operationFacts Table – operation
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Facts Table – operationFacts Table – operation
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Facts Table – operationFacts Table – operation
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Facts Table and Charts of AccountFacts Table and Charts of Account
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Integration of accounting model and transaction mode
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Summary of data structureSummary of data structure
Technological characteristics:Technological characteristics:
non uniform hierarchynon uniform hierarchy
number of nodes: 61 297number of nodes: 61 297
within number of synthetic accounts: 29 268within number of synthetic accounts: 29 268
max depth: 10max depth: 10
Application characteristics:Application characteristics:
decrees dictionary of General Ledgerdecrees dictionary of General Ledger
dictionary of transaction accountsdictionary of transaction accounts
dimensions of data warehousedimensions of data warehouse
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OutlineOutline
Problem of AML – the state of the artProblem of AML – the state of the art
Fundamental aspects of AML system designFundamental aspects of AML system design
System for Analysis and Registration of TransactionsSystem for Analysis and Registration of Transactions
Architecture of data warehouseArchitecture of data warehouse
Case study – examples of a few selected problemsCase study – examples of a few selected problems
Conclusion and future researchConclusion and future research
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Case Study – some statisticsCase Study – some statistics
sample databasesample databasenumber of processed records (daily):number of processed records (daily):
min: ~1,000 rec. (weekend)min: ~1,000 rec. (weekend)max: ~300,000 rec. (end of month)max: ~300,000 rec. (end of month)
monthly (January 2008)monthly (January 2008)total: 2,497,280 rec./monthtotal: 2,497,280 rec./monthdaily average : 80,557 rec.daily average : 80,557 rec.DW dimensions: 197,046 rec.DW dimensions: 197,046 rec.
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Characteristics Characteristics of of DW (General Ledger)DW (General Ledger)
13,299,773 rows in Facts Table13,299,773 rows in Facts Table
20,581,733 Cartesian products in OLAP Cube20,581,733 Cartesian products in OLAP Cube
970,987,198 number of OLAP operations 970,987,198 number of OLAP operations executed during recomputing of OLAP cube executed during recomputing of OLAP cube
970.987.198 / 20.581.733 = 47,177 average 970.987.198 / 20.581.733 = 47,177 average number of OLAP operations over registered number of OLAP operations over registered decreedecree
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OLAP Data Warehouse of General LedgerOLAP Data Warehouse of General Ledger
Implementation of the OLAP DW of General LedgerImplementation of the OLAP DW of General Ledger
Fact TableFact TableDimensionsDimensionsOLAP CubeOLAP CubeCube Pivot as changes viewing in OLAP cubeCube Pivot as changes viewing in OLAP cube
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OLAP Data Warehouse OLAP Data Warehouse – – General LedgerGeneral Ledger
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OLAP Data Warehouse OLAP Data Warehouse – – General LedgerGeneral Ledger
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OLAP Data Warehouse OLAP Data Warehouse – – OLAP Raport with OLAP Raport with view on the Charts of Account dimensionview on the Charts of Account dimension
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OLAP Data Warehouse OLAP Data Warehouse – – OLAP Raport with OLAP Raport with view on the Charts of Account dimensionview on the Charts of Account dimension
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Data Warehouse – Cube PivotData Warehouse – Cube Pivot
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OLAP operation
DimensionGeneral Ledger
DimensionClient
Dimension General Ledger
Dimen.Time
Dimension Time
DimensionClient
Cash in ZLP
Cash in ZLP
Account 1
Account 1 Account N
Account N
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OLAP Data Warehouse OLAP Data Warehouse – – OLAP Raport with OLAP Raport with view on the Charts of Account dimensionview on the Charts of Account dimension
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OLAP Data Warehouse OLAP Data Warehouse – – OLAP Raport with OLAP Raport with view on the Charts of Account dimensionview on the Charts of Account dimension
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OLAP Data Warehouse OLAP Data Warehouse – – General LedgerGeneral Ledger
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PerformancePerformance of of DW DW SART OLAPSART OLAP
Data import: 57,952 decreesData import: 57,952 decrees
OLAP recalculation:OLAP recalculation:OLAP calculation: 4 min. 10 sec (250 sec.)OLAP calculation: 4 min. 10 sec (250 sec.)OLAP operations: 5,275,770OLAP operations: 5,275,770number of created Cartesian products OLAP: number of created Cartesian products OLAP: 991,675991,675average number of OLAP operations/sec: average number of OLAP operations/sec: 21,103.1 op./sec.21,103.1 op./sec.
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PerformancePerformance of of DW DW SART OLAPSART OLAP
OLAP reporting performance:OLAP reporting performance:
Chart of Accounts dimension OLAP view:Chart of Accounts dimension OLAP view: Maximum: ~2 sec.Maximum: ~2 sec. Average: ~0.6 sec.Average: ~0.6 sec.
Time dimension OLAP view:Time dimension OLAP view: Maximum: ~1.2 sec.Maximum: ~1.2 sec. Average: ~0.4 sec.Average: ~0.4 sec.
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Summary of Summary of DW DW architecture the SART OLAParchitecture the SART OLAP
Data Warehouse model based on non-uniform Data Warehouse model based on non-uniform dimensionsdimensions
OLAP model based on non-uniform dimensionsOLAP model based on non-uniform dimensions
Cube Pivot operation with slice functionalityCube Pivot operation with slice functionality
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SART – Transaction mergingSART – Transaction merging
Transaction merging processTransaction merging process in SART in SART
BuildBuildinging a a transaction model based on the transaction model based on the General Ledger decreesGeneral Ledger decrees
IntegrationIntegration of of the the transaction model with transaction model with the the General Ledger accounting modelGeneral Ledger accounting model
Integration Integration of the of the transaction model with transaction model with a a OLAP reportingOLAP reporting
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SART – Transaction mergingSART – Transaction merging
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SART – Transaction mergingSART – Transaction merging
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SART – Transaction mergingSART – Transaction merging
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SART - Cash Flow Chains AnalysisSART - Cash Flow Chains Analysis
Cash Flow Chains Analysis (CFCA)Cash Flow Chains Analysis (CFCA)
Cash Flow ChainsCash Flow Chains
OLAP Data Warehouse of Cash Flow ChainsOLAP Data Warehouse of Cash Flow Chains
Cash Flow Chains Analysis – example of useCash Flow Chains Analysis – example of use
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SART - Cash Flow Chains AnalysisSART - Cash Flow Chains Analysis
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K1
K2
K3
K4
k14
k15
k16
k17
K8
K6
k10
k12
K5
K7
k11
k13
K9
T8
T9
T6
T5
T1
T18
T20
T19
T14
T16
T2
T11
T7
T12
T3
T17
Source accounts End accountsIntermediate accounts
T10
T15
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Cash Flow Chains AnalysisCash Flow Chains Analysis
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K1
K2
K3
K4
k14
k15
k16
k17
K8K6
k10
k12
K5
K7
k11
k13
K9
T8
T9
T6
T5
T1
T18
T20
T19T14
T16
T2
T11
T7
T12T3
T17
T10
T15
K1
K2
K3
K4
k14
k15
k16
k17
K8K6
k10
k12
K5
K7
k11
k13
K9
T8
T9
T6
T5
T1
T18T20
T19
T14
T16
T2
T11
T7
T12T3 T17
T10
T15
K1
K2
K3
K4
k14
k15
k16
k17
K8K6
k10
k12
K5
K7
k11
k13
K9
T8
T9
T6
T5
T1
T18
T20
T19T14
T16
T2
T11
T7
T12T3T17
T10
T15
K1
K2
K3
K4
k14
k15
k16
k17
K8K6
k10
k12
K5
K7
k11
k13
K9
T8
T9
T6
T5
T1
T18T20
T19
T14
T16
T2
T11
T7
T12
T3
T17
T10
T15
K1
K2
K3
K4
k14
k15
k16
k17
K8K6
k10
k12
K5
K7
k11
k13
K9
T8
T9
T6
T5
T1
T18T20
T19
T14
T16
T2
T11
T7
T12
T3
T17
T10
T15K1
K2
K3
K4
k14
k15
k16
k17
K8K6
k10
k12
K5
K7
k11
k13
K9
T8
T9
T6
T5
T1
T18T20
T19T14
T16
T2
T11
T7
T12T3
T17
T10
T15
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Transaction chains – trees viewTransaction chains – trees view
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Transaction chains – trees viewTransaction chains – trees view
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Transaction chains (cash flow)Transaction chains (cash flow)
22 921 13 359
2 738
22 921 13 359
26 830
5 4
44
2 738
22 921 13 359
26 830
5 4
44
2 738
6 9
59
22 921 13 359
26 830
5 4
44
2 738
6 9
59
26 010
16 704
22 921 13 359
26 830
5 4
44
2 738
6 9
59
26 010
16 704
14 037
18 214
18 215
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SART - Cash Flow Chains AnalysisSART - Cash Flow Chains Analysis
Four major CFCA ratesFour major CFCA ratesSource Accounts/Transaction chains ratioSource Accounts/Transaction chains ratioDestination Accounts/Transaction chains ratioDestination Accounts/Transaction chains ratioInner Accounts/Transaction chains ratioInner Accounts/Transaction chains ratioNumber of account cycle chainsNumber of account cycle chains
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CASE STUDYCASE STUDYSART - Cash Flow Chains AnalysisSART - Cash Flow Chains Analysis
Sample database of OLAP Data Warehouse Sample database of OLAP Data Warehouse CFCACFCA
Number of transactions : Number of transactions : 4646,,459459
Number of accounts in CFCA: Number of accounts in CFCA: 3838,,844844
Number of chains: Number of chains: 55,,021021,,459459
Number of chains links: Number of chains links: 2929,,567567,,581581
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CASE STUDYCASE STUDY
Analysis of Transaction ChainsAnalysis of Transaction Chains
In the case study it will be analyzed In the case study it will be analyzed transaction chain from the source account transaction chain from the source account (id=(id=2292122921) to the target account ) to the target account (id=(id=14037).14037).
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CASE STUDYCASE STUDY
Transaction Chain AnalysisTransaction Chain Analysis
Report characteristics: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”.Important risk of ML using „shell companies”.
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Cash Flow Chains Cash Flow Chains Analysis Analysis
Wybrane konto źródłoweWybrane konto źródłowe
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Cash Flow Chains Cash Flow Chains Analysis Analysis
Wybrane konto źródłoweWybrane konto źródłowe i docelowe (wynik zapytania) i docelowe (wynik zapytania)
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Cash Flow Chains Cash Flow Chains Analysis Analysis
Wybrane konto źródłoweWybrane konto źródłowe i docelowe (wynik zapytania) i docelowe (wynik zapytania)
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Cash Flow Chains Cash Flow Chains Analysis Analysis
Wybrane konto źródłoweWybrane konto źródłowe i docelowe (wynik zapytania) i docelowe (wynik zapytania)
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Cash Flow Chains Cash Flow Chains Analysis Analysis
Wybrane konto źródłoweWybrane konto źródłowe i docelowe (wynik zapytania) i docelowe (wynik zapytania)
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Cash Flow Chains Cash Flow Chains Analysis Analysis
Wybrane konto źródłoweWybrane konto źródłowe i docelowe (wynik zapytania) i docelowe (wynik zapytania)
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Cash Flow Chains Cash Flow Chains Analysis Analysis
Wybrane konto źródłoweWybrane konto źródłowe i docelowe (wynik zapytania) i docelowe (wynik zapytania)
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Cash Flow Chains Cash Flow Chains Analysis Analysis
Wybrane konto źródłoweWybrane konto źródłowe i docelowe (wynik zapytania) i docelowe (wynik zapytania)
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Cash Flow Chains Cash Flow Chains Analysis Analysis
Wybrane konto źródłoweWybrane konto źródłowe i docelowe (wynik zapytania) i docelowe (wynik zapytania)
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Conclusions and future worksConclusions 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
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