Know Your Customer(KYC) / Onboarding

Assess & Implement

• Process, Systems & Internal KYC/AML
Back Office Systems External Data Source
Software Systems
Understanding
• Orchestrate the
process
• Workflow
RPA Implementation

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1 Customer
Sanctions Terrorist Theft, Fraud
Due Diligence Name Screening
Screening Financing Assessment
(CDD)
Validation & Testing
2 Enhanced
Politically Exposed Person (PEP) Adverse Media / Negative news
• Test Cases Due Diligence Checks screening
(EDD)
• Test Scenarios
• Shadow runs 3 Risk Application of AML Risk Generation of
Assessment Risk Rating Assessment Risk Score

Monitor & Control 4 Documentation Data Matching & Matching Exclusion

KYC Scoping Cleaning Mechanism Criterion
• Documentation
• Enhancements 5 Customer Personalized Customer
• User Education Segmentation banking offers Categorization

• Internal KYC analytics process development • Development of automated compliance reports

CRISIL GR&A
• Tolerance setting and validation (feedback) • Ad-hoc analysis of tuning and performance
also supports
• Building of in-house name screening algorithms • Extension to residential and document level matches

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 © 2017 CRISIL Ltd. All rights reserved.
Case Study – AML Transaction Monitoring
Case Study: AML Transaction Monitoring (1/2)

• Develop and validate the current standardized customer segmentation model for a large bank holding company
headquartered in the UK
Objective • Develop a methodology to determine thresholds for each segment
• Develop and execute scenarios to generate alerts and transactions, by tuning the parameters for thresholds

Client Impact
Portfolio II
• The implementation was done globally across countries, with
Portfolio X Portfolio IV integration of local regulations

© 2017 CRISIL Ltd. All rights reserved.

• The bank was able to meet the timelines of this validation
Portfolio V
• The suite of tools developed could be reused (with little or no
Portfolio III Portfolio I modifications) for future validation exercises
• Risks associated with failed segments were quickly mitigated
• Coverage redundancy reduced due to improved segmentation and
CRISIL scenario tuning
Validation • False positives reduced by 5% due to threshold tuning
Standar-
dization Framework
Execution Highlights
Segmentation Validation:
Para- • Generalized Validation Framework
meterized
 Developed a suite for validating the segmentation model for
standardized segments and risks for different portfolios
Automation  Gained an in-depth understanding of various input parameters for
different portfolios to derive common ‘plug-in’ solution for
validation tasks
• Automated Reports
 Produced automated validation reports for portfolios
 Automated deep dives for root-cause analysis for segmentation
validation failures
Validation Root Cause
 Provided scalable reporting aspects to monitor/include additional
Reports Analysis
KPIs

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Case Study: AML Transaction Monitoring (2/2)

Threshold Tuning Approach Execution Highlights

Threshold Review & Tuning

Thresholds • Variation in parameters’ thresholds
• Determine relevant basis population for each scenario
based on
Scenarios • Tunable parameters changed  Transactional level scenarios
 Non-transactional level scenarios
• Genuine alerts
• Derive and validate thresholds based on tunable

© 2017 CRISIL Ltd. All rights reserved.

Alerts
• False positives
parameters to ensure alert rules for scenarios are met
• Have a QC process to verify the threshold bands selected
Scenario Tuning Approach and to verify events and alerts generation
Scenario Testing & Tuning
Region Channel
• Scenario development/modifications based on the
Customer Risk
following parameters (not extensive)
Data Business
Activity  Customer segment
 Geographies, channels and risk levels
 Line of banking business
• Script development  Amendable transaction patterns
Scenario • Script validation
Development
• Scenario Validation to confirm
• Default and below threshold
analysis  Business requirements, specifications and
transaction/alert counts are met within the select
scenario
 Coverage and redundancy aspects between scenarios
• Accuracy
QC & • Develop a threshold table for each scenario to analyze or
• False positives
Tuning • Coverage redundancy modify parameter threshold based on feedback