aml and compliance analytics
TRANSCRIPT
A Disrupting Technology for Compliance
A New Approach to Mitigating Risk
The Latest Tool for the Chief Compliance Officer
AML and Compliance Analytics
January 2016
www.Telavance.com
Challenges Facing Today’s Compliance OrganizationWhat do we see at Financial Institutions?
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• Increasingly complex and more Money Laundering
• Increasing organized financial fraud
• Increased regulatory focus on AML and KYC including focus on Model Validation, Optimization & Quantitative Analysis by regulators
Multiple sources of transactions systems
Multiple compliance systems by LoB, Geography
Challenges with proprietary vendor systems
Rising Cost of Compliance
Huge risk and financial implications of non-compliance
Increasingly complex and more Money Laundering
Increasing organized financial fraud
Increased regulatory focus on AML and KYC including focus on Model Validation, Optimization & Quantitative Analysis by regulators
Telavance, Inc. www.telavance.com
Analytics - An Approach to Address Today’s Challenges
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How does Analytics help?
Data
Historical View
What is the number of alerts?
Which products or channels generated the alerts
How did we perform the alerts
Current Situation
How are we currently doing
What is our current risk profile
Forward looking
Key Risk Indicators
What-if analysis
Actionable Insights
Opens the “black box” of AML systems
Science
Discover
Insights
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What is Analytics?
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making – Wikipedia
Analytics leverage data in a particular functional process (or application) to enable context specific insight that is actionable –Gartner
Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. – Whatis
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Types of Analytics
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Prescriptive Analytics
Descriptive Analytics
Dashboards and Reports
Predictive Analytics
Recommends one or more courses of
action
The purpose of descriptive analytics is
to summarize what happened.
Utilizes a variety of statistical, modeling, data mining, and other techniques to study recent
and historical data, and using it for future trends and behavior patterns
Telavance, Inc. www.telavance.com
How Analytics is Applied to Today’s Challenges
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Risk
Management
Operational
Efficiency
Costs
Mitigates risks by analyzing effectiveness of rules/ models
Advanced risk identification and assessment
Effective risk management
Improve program efficiency
Investigation efficiency
Reduce compliance costs
Improved decision making
Reduce compliance costs
Improved management reporting
Telavance, Inc. www.telavance.com
Telavance Analytics Framework
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Products Channel
Calendar Location
Party
Transactions
Customers,
Accounts
‘What’ – Product
‘Where’ – Geography
“Who’ – Party, Organization
“When’ – Calendar
Data Model Models and Framework Insights
‘Events’ – Transactions, Alerts, Cases, SARs, etc
‘How’ – Channels
Example Use Cases
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Analyze different facets of the transactional information
Analyze historical patterns
Forecasting/Predictive Analysis
Behavioral Profiling
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Descriptive Analytics
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Analyze transactional characteristics related to case(s)Countries involved and their risk levelCounterparties and their risk levelTransfers and roles of each transfer pointProduct types usedParties relations
Case Research
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Analyze AML rules and operating parameters
Model rules validation
What-if Analysis
Model statistical evaluation
Productivity Analysis (ATL, BTL Testing)
Model Analysis
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Model Risk Management Analytics
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In-depth visual display of the relationships that exist for a party that is the subject of a compliance review.
Parties may be related to other parties, peer groups, business segments, and to the overall financial institution.
Relationship Analysis
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Operational Analytics
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Number of Alerts by employees
Number of cases by employee
Alert productivity
Cases productivity
Top nn Investigators with open cases
Employee
Productivity
Periodic
Production
Reports Alerts generated vs Alerts closed
Cases opened vs Cases closed
Number of 90 day reviews completed
Number of 90 days review coming up in next 15 days
Number of SARs filed
Number of SARs to be filed in next 10 days
SARs by aging
Alerts
Volume of alerts by category/ sub-category
Closed alert count
Alerts created over time
Alert Status count
False Positive and Investigation over time/ category/ channel
Case Lifecycle
Cases by risk category
Cases by category
Cases by department
Cases by status
Closed cases by aging
Time series analysis
Closed cases over a period of time
Cases created by type
Number of cases over a period
Top 5 open cases by category
Open cases by status
Open cases by aging
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Operational Analytics
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Data Profiling and Quality
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Demonstrates proper understanding of the source data for Risk Rating and Transaction Monitoring
Exposes data quality issues if anyIncorrect data mapping from core systems (Parties on wire transfer)Lack of critical data for purposes such as risk rating, monitoring
ExamplesDistribution of customers by risk classDistribution of customers by industry codeDistribution of transactions by countryDistribution of transactions by amount
Data Profiling & Quality
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Data profiling is the process of
analyzing an existing data source to
provide statistics and information
about the data.
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Distribution of customers by risk classData Profiling - Examples
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4%
23%
6%67%
Customers by Risk Class
Default MED HIGH LOW
14%
21%
63%
2%
Transaction Count by Risk Class
MED
Default
LOW
HIGH
53%26%
21%
0%
Transaction Amount by Risk Class
MED
Default
LOW
HIGH
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Transaction Distribution by Customer CountryData Profiling - Examples
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29
36
12
52
11
31
79
4
70
6
27
4
26
8
24
1
23
6
14
1
13
3
77
41
37
34
33
26
22
21
17
14
13
13
11
9 4 4 4 4 4 2 2 2 2 2 1 1
0
500
1000
1500
2000
2500
3000
3500
CA
NA
DA
AU
STR
IA
UN
ITED
KIN
GD
OM
IREL
AN
D
PO
LAN
D
HO
NG
KO
NG
VEN
EZU
ELA
BR
AZI
L
CZE
CH
REP
UB
LIC
MEX
ICO
FRA
NC
E
UK
RA
INE
LUX
EMB
OU
RG
BER
MU
DA
HU
NG
AR
Y
CH
INA
CA
YMA
N IS
LAN
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TUR
KEY
AR
GEN
TIN
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CU
BA
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FAC
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SWIT
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LAN
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EDER
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ITED
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Transaction Distribution by Customer Country
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Transaction volume an amount distribution by originating country Data Profiling - Examples
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Telavance, Inc. www.telavance.com
Service Offerings
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AnalyticsData Quality and Profiling
Compliance Risk Analytics
Compliance Performance Analytics
Define and deliver key performance indicators to achieve higher operation efficiency
Visual Analytics
Visual data discovery, exploration and interaction for analytical insights and meaningful decisions
Investigative Analytics
Case Analytics
Relationship Analytics
Model Risk Management Analytics
Enhance model management
Model calibration and tuning with sandbox
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Naveen [email protected]