Oracle Watchlist Screening

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Oracle Watchlist Screening

Topics

•

Screening trends & needs

•

Increasing screening data accuracy

•

Reducing false positives

•

Screening international data

•

Prioritizing highest potential risks

•

Shortening review cycles

The 2

nd

_{Wave of Compliance}

2002 - 2008

2009 - 2015

Transaction Modelling (TM)

• Detect suspicious behaviour • Pattern recognition • Statistical modelling • Behavioural analysis • ££M costs Transaction Screening (TS) • Message Parsing

• Watch list filtering (basic) • ££M costs

Many failures:

• Troubled/lengthy deployments • Missed Sanctions hits

• False Positives +++

Leading the 2

nd

_Wave

2002 - 2008

2009 - 2015

Customer Screening (CS)

• Sanctions, PEPs, Risk screening • Vast quantities of customer data

• Disparate systems, data structures, formats, languages, alphabets

• Breadth + depth of data analysis & search capabilities

• Intelligent data optimization recognized as critical • Advanced algorithms required for data variance,

anomalies, data quality, transliteration and transcription

• Widespread recognition that greater accuracy = large reduction in false positives

• Compliance cost reduction • S-M-L: all sizes - all sectors

• not just for Tier 1’s

Transaction Modelling (TM)

• Detect suspicious behaviour • Pattern recognition • Statistical modelling • Behavioural analysis • ££M costs Transaction Screening (TS) • Message Parsing

• Watch list filtering (basic) • ££M costs

Many failures:

• Troubled/lengthy deployments • Missed Sanctions hits

• False Positives +++

2

nd

_{Wave Requirements:}

1. Maximum Operational Effectiveness • False positives – dramatic reductions

• High accuracy

• Any data / anywhere

• Zero screening limitations (frequency, volumes, watch lists)

• Deploy anywhere

• Multi-lingual, multi-cultural environment 2. Optimum Cost Efficiency

• Risk Scoring, Case Management: reduces review (cycle) time

• Compliance workflow, case routing: speeds case workload

• Advanced language capabilities: reduces costs for international business • Scales from a laptop to the Data Centre

New markets...new challenges

POCA FCPA

UK Bribery Act

Increasing Legislation, Regulation & Guidance

FATF JMLSG

USA PATRIOT Act EAR Customers Partners Suppliers _Employees Expanding Boundaries of Risk Ports Vessels Contractors

Universal

Risk Screening

Requirements

3PL/Trade Export Broader Markets Facing Legislation

MSBs Auto Finance

Tobacco Law Firms OFAC

Introducing Oracle Watchlist Screening

• What is it?

– A comprehensive risk and compliance screening application – Chosen by over 160 customers globally

– Agnostic to data being screened – Screens in batch and real-time

– Comprehensive reviewer and management tools

• How does it benefit you?

– Reduces the cost of meeting compliance obligations – Reduces the chances of undetected risk sources – Simplifies screening of international data

– Minimizes vendor dependency

Accurate Data = Accurate Screening

1. ‘Overfilling’ of name data

2. Poor spelling of name and address information 3. Multiple names stored in a single field

4. Name information ‘misfielded’ into addresses 5. Date of Birth information in various formats 6. Entities and individuals mixed together 7. Non-standard name constructs

8. Poorly fielded address information 9. Non-standard country information

Customer Data Preparation

Understand

Structure

Re-structure

Standardise

Enrich

Down

load

ISO

Country

Alias

Tagging

Name

Analytics

Occupation

Analytics

Dedupe

Country

From City

Gender

Analytics

YOB

Inference

Country

Inference

Phrase Profiling

Entity

Hints

Look ups

Spellings

Misused

Fields

Split

Records

Linguistic

Equivalency

Phone No.

Analytics

Email

Analytics

Simplifying international data screening

•

New geographies – new risks

– PEPs and their associates

– Embargoed Countries

•

Multiple writing systems in use

•

Oracle Watchlist Screening

– Optional Language & Country Packs – 45 languages & 57 countries

–Cyrillic

–Greek

–Chinese

–Arabic

–Hangul (Korean)

–Kanji, Hiragana and Katakana (Japanese)

Transliteration Example: Greek

•

Mostly character level but with some complex rules

•

Uses the standard EDQ Transliterator processor with the Greek

to Latin option, and a few simple additional rules (removing

Name Variant Recognition Example: Russian

•

Dictionaries are provided to recognize and

match different transliterations of the same

Russian name (that might exist on lists) >>>

Transcription Example: Arabic

•

For Arabic, transliteration does not

work

– e.g. دمحم is transliterated by most

methods to ‘mhmd’ but really

represents ‘Muhammad’

•

Direct transcription is required. This

uses a dictionary which recognises

more than 200,000 names, and over

5m transliterated variants of those

names

Prioritizing highest potential risks

•

Risk Scores provide measures of relative risk of doing business with

individuals or entities

•

Ensures priority is given to reviewing high risk individuals/entities

•

Automates the closure of low confidence/low risk matches avoiding

unnecessary review work

•

Ensures effective use of resources by prioritising work activity

•

Reduces the burden maintaining PEPs whilst continuing to demonstrate

Externally calculated Risk Scores may be used – for example

Safe Banking Systems Risk Score

Original Watchlist

record

SAFE EI risk score

Filters provide links to investigators

work queue

Cases ranked in priority order presenting highest Shortcuts to

workflow MI reporting

Configurable Workflows

•

Standard 4-Eye Workflow

Match rules tells us why this case has been flagged as a potential match Customer

Data

Watchlist Data

Hyperlinks to Watchlist profile, web search & other external

sources

Click to confirm match decision and add comments

Monitor progress of case investigations

Manage workload across the team

Realtime screening of individuals and entities

Match score and risk score highlight degree of

risk

Link to Case Management for fully audited and tracked