POSEIDON AML

Anti-Money Laundering and Counter- Terrorist Financing System with Anti-Fraud Functionality

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AGENDA

General - Introduction

Regulatory (Compliance) background

International Standards

Application : Poseidon AML

Q&A

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GENERAL - INTRODUCTION

Global perspective

AML

CTF

AntiFraud

KYC

Unified (AML/ATF/Case/A-Fraud)

Modular

Real Time

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Scoring ProfilingWatch

Lists

Cases &

Workflows

Screening

& Alerting

Reporting

& MISKYC

KEY POINTS

Live (real time) scoring

Direct matching over watch lists

Custom/designated rules

Custom/designated groups

Reporting System

Case management

Unified platform (CRM, Reporting,

Management/3rd party)

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PoseidonEU / FATF Compliance

Ease of use

Adaptive to change

Agile

Environment/rules/landscape

Multilingual

POSEIDON AML ON BANKING

POSEIDON®AML is a certified (Bank of Greece 2013, Deloitte 2013) integrated banking solution for the prevention of money

laundering and financing of terrorist organizations. Poseidon can alternatively work as an Anti-Fraud System.

The introduction of new and stricter rules and procedures forces all the banks to set in high priority the issue of security.

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QUOTES FOR POSEIDON

“… Money laundering is a real financial risk for the organizations, related directly to the protection of their reliability.

Natech has the requisite know-how and software in order to deal with these risks.” Deloitte 2013

“ The application has unlimited expandability abilities in order to cover the needs of regulatory conformity of credit institutions. Poseidon is a modular yet transparent system offering Screening, Scoring, Monitoring, Profiling (trader & transactions based - KYC) and Case management functionality. “ BoG, 2013 evaluation

“The system is designed to operate real time on a 24/7 basis utilizing Natech’s uniquely developed matching, scoring and prediction algorithms.” , Greek Postal Office (ELTA) , 2014

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COMPLIANCE

Requirements

Classification

Business Activity

Customer Type

Transaction type

Customer to Institution Relationship (loyal, passing, active etc)

Risk transactional behaviour

Product risk assessment

Chanel risk assessment

Web, ATM, cash, Mobile etc

Standardisation of procedures

Poseidon AML

Automatic Classification

Multiple criteria

Predefined or custom based rules and categories

Calculated transaction behavioural patterns

Custom defined

Assessment & Evaluation

products

Accounts

Business Activity /Information

Multiple Channel real time and batch assessment

Standardisation of procedures and rules

Case management System

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EU (FATF BASED)

3691/2008

•“Prevention and

Suppression of

Money

Laundering”

2577/2006

•“Basic financial

organization

principles”

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KEY POINTS ON THE PROCESS

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Due Diligence

•Intermediary

•Bank associate/staff

•Branch

Customer policy

evaluation

Enhanced Due

Diligence for High –

risk

individuals/companies

Sanction List filtering

(transactions)

Scenario scoring

rules (transactions)

Integration among

core and 3rd party IT

systems

Cash flow monitoringFraud prevention

mechanisms in placeCase Management

DIVERSIFICATIONREAL TIME ASSEMENTS

•On the fly indication/profiling

•Near 0 false indications

Ability to customize

behavior

•Immediate adaptability to new or

improved rules and process

Case management System

in place

•Ability to operate in the UK or in

the EU

Hybrid Case reasoning,

Rule Based System

Ability to built on system

BATCH PROCESSING Next day evaluation

Varying results

Usually follow predefined set up Slow reaction to change /

incremental updates

3rd party systems or no systems at all FCA requires C.M.S. (hefty fined

otherwise)

Either Rule or Case based systems

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KEY FACTS

10 Financial

Institutions run on

Poseidon AML

Most complex AML

implementation in

Greece runs on

Poseidon AML

Poseidon is the only

solution for

AML/CTF/Anti

Fraud and FATCA in

Greece

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KEY FACTS OF A CASE STUDY

Case study of the most complex AML Implementation in Greece

~1.400 transactions / minute

•(7.000 / 5 minutes)

Over 500 Points (Branches /

agents)

Over 10.000.000 clients

EU, OFAC, Internal Black List

Integrated with different 6

payment systems

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REGULATION– BRIEF1

Reporting of suspicious transactions

Record keeping of procedures and of cases

Internal procedure auditing

Transaction scoring and evaluation

KYC – Customer profiling

Due Diligence vs Actual Data

Evaluation and monitoring of money flow

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REGULATION– BRIEF 2

Continuous monitoring of customers with previous suspicious transactions

Integration with Case Management System

Bookkeeping and actual auditing procedures

Customer risk classification (layered)

Transaction risk classification (layered)

Chanel risk classification (layered)

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ENTITIES – THE BRICKS AND PILLARS

RESULTS

ENGINE

RULES GROUPS OBJECTS

DATA

LISTS PATTERNS HUMAN INPUT TRANSACTIONS

CASE

SYSTEM

PROCESSES DOCUMENTS

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ENTITIES

Groups

Objects with common characteristics (can be defined) Employed customers

Customers with income over 15K $

People living in specific areas

Political exposed people

Men/Women/Children

People lacking justification documents

Objects

Meanings Deposit

Payment

Insurance

Unusual transaction amount

Transaction amount

Relationship among different clients

Has a spouse / relationship

Employed/Unemployed

Chanel

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GROUPS• Custom defined

groups with common characteristics

• Risk assessment per group

• Ability to define level of suspiciousness • Eg customers with low

declared income and deposits over 200.000$

• Eg customers with low income and transactions mounting over 100.000$

• Customers with missing information

• Declaration of common business activities

• Assignment on (watch lists)12/3/2015 NATECH S.A. 17

OBJECTS

Characteristics based on which

the system classifies entities

and assigns risk factors

Basic risk assignment object

in Poseidon AML (definition

of entities)

Example

•Entity is customer

•Entity is loyal

•Product

•High risk product

•Transaction type

•Chanel type

Are parts of the group

Classify entities

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RULES

Scoring

algorithms

•Predefined

•Custom based

Declare the “risk”

level of each

group

Applied in

•Groups

•Objects

•Transactions

•Procedures

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TECHNICAL

Data

SQL Server / Oracle 11g RDMBS

Heuristic Matching

Hybrid (Case-feedback) quality matching

Hybrid Case Based Reasoning (Rules and case based))

General

XML messaging

N-tier architecture (multi)

Active Directory Authorization

Ability to integration with MS Exchange for case management

Multi level authentication and authorization capabilities

Built on Natech DRP12/3/2015 NATECH S.A. 20

PARTICLES

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Scoring ProfilingWatch

Lists

Cases &

Workflows

Screening

& Alerting

Reporting

& MISKYC

INTEGRATION AML – CRM –3RD PARTY SYSTEM

3rd Party

(origination

system)

Watch Lists ScoringRisk

EvaluationCRM

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POSEIDON AML THE PROCESS

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KEY POINTS ON THE PROCESS

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Due Diligence

•Intermediary

•Bank associate/staff

•Branch

Customer policy

evaluation

Enhanced Due

Diligence for High –

risk

individuals/companies

Sanction List filteringScenario scoring

rules

Integration among

core and 3rd party IT

systems

Cash flow monitoringFraud prevention

mechanisms in placeCase Management

ACTUAL TRANSACTIONSFLOW

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TranscationTransaction

Auditing

•Watch Lists

•Rules

•Statistical

evidence

Scoring Profiling

•Group

•Sector

•Client

•Transaction

KYCEvaluat

ion

Alert

Scree

nCase

DESIGN CHARACTERISTICS

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Poseidon AML utilizes

Natech’s Core Banking

Authentication system which

supports multiple

authentication protocols,

encryption mechanisms and

heuristic algorithms to ensure

data and content rights are

managed accordingly.

Security and Integrity

POSEIDON® uses a heuristic

lexical analysis based on

custom developed Lucene

Alrogithms to match

customers belonging to

(Watch lists). Minimizing false

positives and false negatives,

Poseidon is a world leader

Matching

A hybrid of a case

management system and a

Rule based system empower

Poseidon making it one of the

fastest and most reliable

solutions fighting Money

laundering and Terrorist

Financing Actions. Combining

historical statistical evidence

together with the information

flown from every integrated

system, the system scores

each and every transaction,

the customers (both parties

if exist), the originator, the

groups the customers belong

to but also the risk factor of

the transaction type. The

scoring system also

incorporates information

from the watch lists and

evaluates all factors deriving

from the custom rules set.

Multiple regression

algorithms allow multiple

decision thresholds to be

evaluated and acted upon.

Scoring

POSEIDON ® AML supports

the centralization of

configuration, setup and

handling of all systems

functionality. Thus, every

detail is taken care of from a

central point of view

Centralized

Administration

Over 10 financial institutions

(7 banks, 2 payment

institutions and 1

international paymenet

agency) run on Poseidon AML

to curb Money Laundering

and Terrorist Financing

activities. Poseidon has been

designed to support flexible

organizations at an

international level scoring

over 10.000

transactions/minute per CPU

Adherence to

international standards

BA

SIC

AR

CH

ITE

CT

UR

E

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PERIPHERAL STATIONS

COMMUNICATION WITH CORE

Flat files

XML files

Common

(shared) Table

structure Proxy

server

Database -

Trigger based

– queue based

Excel / csv files

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DEMO SCREENS TYPICAL FLOW

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DUE DILIGENCE - KYC

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KYC – CUSTOMER GROUPS

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DUE DILIGENCE – KYC- NETWORK

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ENHANCED DUE DILIGENCE –NETWORK

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CUSTOMER DATA - BEHAVIOUR

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CUSTOMER DATA - BEHAVIOUR

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Expected behaviour

Customer behaviour

FILTERS

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CUSTOMER -TRANSACTIONS OVERVIEW

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CUSTOMER OVERVIEW

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SUSPICIOUS CUSTOMERS BY DATE

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AML CASE HANDLING

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AML CASE HANDLING

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AML CASE HANDLING

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SUSPICIOUS TRANSACTIONS Instructions

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MODIFYING RULES AND OBJECTS

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CHANGING RULES

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INTERNAL BLACK LIST

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PARAMETRES SETTINGS

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DATA MANAGEMENT SETTINGS

AML/CTFRegulatory background (Compliance)

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Rules setting

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team

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DOCUMENTS

According to FATF guidelines transactions with elevated risk characteristics should be supported by justification documents for verification and classification purposes

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THANK YOU

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