Download to read offline
Watch list screening
- 1.
- 2. Watchlist Screening • Чтоэто такое: – Бизнес-процесс выявления потенциального совпадения между записью клиента или любой другой (клиент, поставщик, бизнес-партнёр, сотрудник) которая может появиться в публичном или частном ‘списке’ – Возможность избежать ведения бизнеса с нежелательными физическими или юридическими лицами • Почему это важно: – Международные и региональные законы • Борьба с отмыванием денег • Предотвращение финансирования терроризма – Определение потенциального отмывания денег, взяточничества, коррупции и/или финансирования терроризма • Необходимость в решении следующего поколения: – Улучшенные результаты • • • • Лучше выявление соответствий Снижение ложных срабатываний Снижение операционных издержек Лучше масштабируемость “Know Your Customer” (KYC) and “Know Your Customer” (KYC) and Enhanced Due Diligence (EDD) Enhanced Due Diligence (EDD) obligations obligations •• Bank Secrecy Act 1970 Bank Secrecy Act 1970 •• Foreign Corrupt Practices Act 1977 Foreign Corrupt Practices Act 1977 •• Money Laundering Control Act of 1986 Money Laundering Control Act of 1986 •• Terrorism Act 2000 Terrorism Act 2000 •• Financial Services & Markets Act 2000 Financial Services & Markets Act 2000 •• USA Patriot Act 2001 USA Patriot Act 2001 •• Proceeds of Crime Act 2002 Proceeds of Crime Act 2002 •• EU 3rd Money Laundering Directive 2007 EU 3rd Money Laundering Directive 2007 •• Money Laundering Regulations 2007 Money Laundering Regulations 2007 •• UK Bribery Act 2010 UK Bribery Act 2010 •• ... ... 2
- 3. Данные имеют сложнуюструктуру Примерно Примерно 300,000 имён 300,000 имён только в только в списке US списке US OFAC... OFAC... 3
- 4. Данные имеют сложнуюструктуру 19 Aliases and 4 Locations Простого матчинга недостаточно 10 Aliases and 2 Date of Births
- 5. Информация о клиентахимеет сложную структуру Типичные примеры: 1. ‘Overfilling’ of name data • Списки и клиентские данные имеют часто включают непоследовательную, неточную и неполную информацию • Screening against data that is not fit for purpose reduces screening accuracy, increasing risks and costs 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
- 6. Ключевые требования кWatchlist Screening • Оптимизация источников данных (списков с проверочными данными, списков клиентов и пр.) – Любой список из любого источника – Понимание, стандартизация и обогащение • Лучшие в своём классе технологии матчинга – – – – – Эквиваленты имён и альяся Множество языков и скрипты Эффективный масщтабируемый матчинг Открытый настраиваемый механизм матчинга Приоритезация результатов в соответствии с рисками • Управление результатами – Эффективный интегрированное ручное управление – Конфигурирование автоматических правил в соответствии с рисками – Показатели процесса и управление – Аудит 6
- 7. Приложение Watchlist Screening Prepare& Optimize Case Management Understand 500+ match rules Prioritized alerts Structure Aliases/name equivalents Customizable workflow Standardize Watch lists •Public lists (HMT, OFAC, EU, UN, etc.) •Commercial Lists •Private lists Match & Score Translation/ transliteration Reporting & metrics Enrich Match & risk scoring Audit trail Customer lists •Multiple systems •Inconsistent information & standards 7
- 8. Более 500 правилпоиска дубликатов С лёгкой конфигурацией Матчинг использует “бизнесправила” с непонятными комментариями 8
- 9. Работа с международнымиданными • Спецсписки публикуются на латинском языке, однако клиентские данные могут быть на любом языке... • Требования к сканированию: – Транслитерация – Транскрипция – Библиотеки имён в альтернативных формах, прозвища и пр. • Свойства: – Межязыковое выявление – 45 языков и 57 стран • • • • • • Cyrillic Greek Chinese Arabic Hangul (Korean) Kanji, Hiragana and Katakana (Japanese) – Расширенные библиотеки имён • • • • Chinese Korean Arabic Japanese 9
- 10. Эквиваленты имён • Транслитерация– преобразования символов; может теряться фонетическая информация • Транскрипция – преобразование фонетического представления • Перевод – словарные эквиваленты, включая прозвища
Editor's Notes
- #3 What do watch lists include? Watch lists published by government agencies (i.e. HMT in UK) include lists of sanctioned individuals, entities, de-barred countries, terrorist organisations, etc. Doing business with any individual or organisation listed on a government watchlist risks prosecution in the form of serious fines and potentially imprisonment Commercial watch lists published by the likes of Dow Jones, World-Check and others include entries included on government sanction lists as well as what we call “Politically Exposed Persons” or PEPs. Keeping it simple, a PEP can be thought of as a high ranking government or public official and as such, may be at greater risk of being exposed to bribery & corruption. Legislation therefore stipulates that organisations must apply enhanced due diligence when doing business with PEPs to assess the potential risk of facilitating transactions that may represent the proceeds of crime The challenge in watchlist screening is to be able to accurately identify actual sources of risk, whilst minimising the number of alerts presented for review that turn out to be erroneous matches, otherwise known as false positives. This is where Oracle’s Watchlist screening solution differentiates itself
- #5 Here we have the same individual “Abu Bakr” with 10 aliases and showing a DoB of 20th and 10th August 1972 Is this the same person?
- #6 Run through each of the examples here - this is self explanatory
- #8 Its the combination of accurate data preparation and effective screening that makes our product unique Firstly, we can undertake screening both in batch mode and in real-time. We also provide our own real-time UI fully integrated into case management that removes the need for our customers to create their own UI or undertake complex integration projects with customer on boarding systems We also screen against both open source and commercial watch list providers as shown here Then we preparing and optimize data using the data management processes such as “understand”, “structure”, “standardize” and “enrich” as shown – this is the first part of the screening process to ensure customer data is fit for the purpose of screening Then we move into the screening stage shown as “match & score”. Here we are using techniques to review and address variances in watch list and customer data such as different formats used for data of birth, plus use of non standard country codes and name variances. At this stage, we are also normalising any name variances and converting non-Latin names into Latin format ahead of the screening process. Once we’ve done that, we prioritise Alerts using either internally derived risk scores or those available from external providers based on the secondary identifiers included by the watch list providers. Having completed the screening process Alerts are presented to case management for review and resolution, with each type of Alert (whether PEPs, sanctions, prohibitions) being prioritized and having its own tailored workflow to align with the organizations screening policies. Finally comprehensive real-time reports and audit trails are provided to view the current status and progress of any Alerts and facilitate sharing of information with regulatory authorities if necessary
- #9 Now we can see the match rules that we have created to determine when and where we undertake a review of a potential match. These are all configurable by the customer and presented using a simple business narrative. Over 450 match rules can be created using OWS, delivering a high level of screening sophistication..particularly important for minimising the number of matches that turn out to be false, whilst reducing the chances of a true match not being detected You can see that we first look for where there is a match across multiple attributes as this is likely to give us a higher degree of confidence that the customer and watchlist records are for the same individual or entity and we prioritise this according within case management for review. You can see that anything with an exact name match is given a priority of 1 and where there is a match against first name or surname, this is given as priority 2. Those with a lower level of confidence matches are given a lower priority score and will appear lower down the list of alerts for review within the case management view.
- #10 Designed for organisations seeking a global screening solution Uses transliteration and transcription techniques Where client data is held in multiple writing systems – Customers, Suppliers, Partners, Employees Simplifies the process of converting non-Latin client data into Latin form As used by the majority of Sanctions & Watch List Providers Enhances the data optimisation process Leading to more accurate identification of risk And minimising the number of false positives Shortens the time taken to onboard new customers in growth markets 5 different writing systems Further 40 more in the pipeline Multiple name equivalencies Central, Eastern, South East Asia as well as the Middle East
- #11 Here we have an example of using transliteration techniques (character by character conversion) to convert from the Greek native writing system to the Latin equivalent