data quality and compliance

WHITE PAPER: COMPLIANCE

WHITE PAPER / Sanction Lists – A Business Risk EU anti-terrorism regulation and sanction lists firmly in hand: options for the technical implementation of statutory provisions

UNAVAILABLE OR UNSATISFACTORY PROCESSES FOR MATCHING CUSTOM-ER AND PROSPECT DATA AGAINST SANCTION LISTS - A FREQUENTLY UN-DERESTIMATED BUSINESS RISK ENTAILING THE PERSONAL LIABILITY OF THE MANAGEMENT

The implementation of the statutory provisions of the EU anti-terror-ism regulation is still a widely discussed aspect in the context of data quality. This regulation prohibits financial services and assets from being made available to terrorist organizations at home and abroad.

If the latest requirements of the EU anti-terrorism regulation are not complied with, the consequences are predictable. For example, there is a danger that the entire assets of a company are frozen. There is even a danger of being included in one of the sanction lists if one of the individuals or organizations listed there is sup-plied. It should also be considered that there is no legal claim to be removed from the sanction list. Anyone who acts without a second thought here quickly puts the future of the entire company at risk and therefore also that of the employees.

Organizations and individuals who fall foul of the EU anti-terrorism regulation are included in various sanction lists compiled by the European Union and institutions of the United States of America, amongst others.

How can in-house customer master data be matched against the current sanction lists, in order to comply with the regulation? We present a practical approach which matches the current editions of sanction lists against the customer master data in batch or online processing, thereby providing you with certainty with regard to the EU anti-terrorism regulation.

The statutory requirements and their effects will be outlined in the following. Furthermore, the relationship with the data quality in the conventional sense will be described. Finally, the options for practi-cal implementation will be considered. The implementation scenar-ios will be documented with examples and possible downstream processes will be presented.

All c

ompa

ny a

nd p

rodu

ct na

mes

and

logo

s use

d in

this

docu

men

t are

trad

e na

mes

and

/or r

egist

ered

trad

emar

ks o

f the

resp

ectiv

e co

mpa

nies

.

http://www.uniserv.com/en/data-quality-applications/compliance-data-quality.php

http://www.uniserv.com/en/index.php


© UNISERV GmbH / +49 7231 936-0 / All rights reserved.

Contents

Statutory requirements

EU sanction lists: Relationship with data quality

Implementation of the matching

Prerequisites for matching against sanction lists

Requirements for the software

Downstream processes

Example of a complete matching operation

How to contact us

List of references

PAGE 3

PAGE 5

PAGE 7

PAGE 9

PAGE 11

PAGE 13

PAGE 14

PAGE 15

PAGE 15



Statutory requirementsThe terrorist attacks of Al Qaida on the United States of America were the catalyst for passing the EU anti-terrorism regulation. The first UN resolution was passed shortly afterwards.A new aspect in these resolutions and regulations is the fact that particular organizations and individ-uals who are not always connected to individual states are included in the so-called sanction lists and not complete countries.

THE FOLLOWING LIST SHOWS THE MOST IMPORTANT

RESPONSES TO THE TERRORIST ATTACKS OF THE 11TH

SEPTEMBER 2001:

– 28th September 2001: Adoption of UN reso-lution 1373 by the community of states

– 27th December 2001: Regulation (EC) no. 2580/2001 - implementation in the applica-ble Community Law

– 27th May 2002: Regulation (EC) no. 881/2002 - extension to specific individuals/organizations

– In addition, the US legislator claims worldwide validity of its export control regulations wher-ever US goods are traded.

The legislator has also even specified who is responsible for correct compliance with the above-mentioned resolutions and regulations.The responsibilities for supervision on the part of the company are regulated in Article 130 and Article 9 of the German Administrative Offences Act (OWiG) and Article 14 of the German Penal Code (StGB).

Here it states that the management and its author-ized bodies and individuals must take reasonable organizational measures which ensure with suf-ficient probability that no offences are committed wilfully or through negligence. The liability and obligation to notify the authorities is considered in Article 70 of the German foreign trade regulations (AWV). Reference is made here to the German foreign trade law (AWG), according to which the responsible people in the company are personally liable for consequences under criminal law. Such persons can only be exonerated by evidence of a functioning organization and compliance with the requisite and appropriate supervisory measures.

Finally, pursuant to Article 44 of the AWG, there is a general duty for anyone participating in foreign trade to furnish information (Puschke & Hohman, 2008: 33). The executive body for examining the duty to furnish information is the Federal Office for Economic Affairs and Export Control (BAFA). It is not definitively regulated which authority is respon-sible for clarifying uncertainties about the identity of a business partner. However, it is advisable to make inquiries at the BAFA in such cases.Apart from the BAFA, compliance with the EU anti-terrorism regulation is also checked by the German customs authorities, the auditing service of the regional finance office as well as criminal prosecu-tion authorities (e. g. public prosecutor‘s offices).

PURSUANT TO ARTICLE 34 [ … ] THE PEN-ALTY IS A TERM OF IMPRISONMENT NOT EXCEEDING THREE YEARS OR A FINE



CRIMINAL PROSECUTION

In this respect, the consequences of non-compli-ance with the EU anti-terrorism regulation are well worth knowing. The standard penalties also extend to German nationals located abroad. Puschke & Hohmann (2008: 41 and following pages) explain the penalties in detail.

– Pursuant to Article 43, Section 4 of the AWG, the penalty is a term of imprisonment of six months to 5 years in the case of non-compli-ance with the EU anti-terrorism regulation. An attempt at circumvention is also liable to pros-ecution.

– Pursuant to Article 43, Section 6 of the AWG, the penalty is a term of imprisonment of not less than 2 years if actions are carried out on a commercial basis and/or in connection with organized crime, the external security of the Federal Republic of Germany is threatened, the peaceful co-existence of the ethnic and cultural communities is put at risk and/or the foreign relations of the Federal Republic of Germany are put at risk. An attempt at circumvention is also liable to prosecution in this case.

– Pursuant to Article 34, Section 7 of the AWG, the penalty is a term of imprisonment not exceeding three years or a fine. The precise wording here is: “In cases where the offender negligently commits such acts referred to in Sections 1, 2 or 4, he or she shall be lia-ble to a term of imprisonment not exceeding three years or a fine.” According to Puschke & Hohmann (2008: 42), this is the most relevant case in practice.

In addition to the above penalties, however, a levy on revenue (Article 73 of the StGb) and / or a confiscation (Article 36, Section 3 of the AWG, Articles 74, 74d of the StGb) can also be expected.

Other consequences are a revocation of approval by the responsible Customs and Excise Office, an increased risk evaluation in the national IT proce-dure “DEBBI” (decentralised assessment of opera-tors), an increase in the amount of securities and/or sureties, non-consideration for national public contracts, a negative corporate image and more frequent tax audits.In view of the possible penalties and the conse-quences of non-compliance with the EU anti-terror-ism regulation, it is advisable to introduce specific measures which contribute to legal compliance.

All references to the current legal situation are taken from

Puschke & Hohmann (2008). The respective primary literature

should be consulted for further information (see Chapter 6: List

of references).

IN VIEW OF THE POSSIBLE PENALTIES AND THE CONSEQUENCES OF NON-COMPLIANCE WITH THE EU ANTI-TER-RORISM REGULATION, IT IS ADVISABLE TO INTRODUCE SPECIFIC MEASURES WHICH CONTRIBUTE TO LEGAL COMPLIANCE.



EU sanction lists: Relationship with data quality With regard to the practical implementation of the EU anti-terrorism regulation, the issue of data quality should not be neglected. Four data quality and process characteristics are relevant:

It should be considered that this always concerns the quality of the in-house data and the in-house processes. The quality and the creation process of the respective sanction lists are not called into question or discussed here.

TRANSPARENCY

Transparency is defined here as the knowledge and the traceability of all processes concerned with data acquisition, matching against sanction lists, logging and downstream processes in the case of matches.

The following questions are raised in this respect:

– How exactly does the (master) data enter the database?

– What is the data source?

– How is the matching against sanction lists executed?

– Are logs generated for the matching operations and how are they administered?

– What is the process for updating the sanction lists?

– How are matches dealt with?

The relevant legislation already provides informa-tion for some of these questions. However, the question is always about whether the respective regulations are complied with.

CREDIBILITY

Credibility is defined as the trustworthiness and reli-ability of a particular data record. This definition can be extended to the credibility of the processes for matching the in-house (master) data against the sanction lists.

TRANSPARENCY

CREDIBILITY

UP-TO-DATENESS FREEDOM FROM ERRORS






– Can the data source be trusted?

– Is the data source known?

– Is the process for matching the data against the sanction lists reliable?

– Is the source of the sanction lists known and reliable?

– Are the matching algorithms suitable?

FREEDOM FROM ERRORS

Information is error free if it corresponds to reality.With regard to the issue of freedom from errors, the following questions should be asked about the in-house data and the matching process:

– Is the master data error-free? (And is the data quality monitored and optimized if necessary?)

– Has the process for (master) data acquisition been defined, so that suitable rules can be complied with?

In the case of these questions, it becomes evident that a certain quality of the master data is already assumed. If nothing is known about the data qual-ity or if no measures have been taken to guarantee a data quality which is high enough for reliable matching against the sanction lists, the matching result must be called into question.

UP-TO-DATENESS

Information is up-to-date if it represents the actual character of the described object in real-time.Here too there are several questions about the matching against the sanction lists which should be raised:

– Is the data quality of the in-house (master) data adequate for matching data against the sanc-tion lists?

– Are the sanction lists up-to-date?

– Is the technical implementation of the process for matching data against the sanction lists up-to-date?

Finally, the data quality not only concerns the high quality of the in-house master data but also the quality of the processes. This refers to processes which guarantee high quality of the in-house com-pany data and processes which provide effective implementation of compliance with the EU anti-terrorism regulation. It becomes quite clear here that the quality of the company data plays a big role when it is matched against the sanction lists. In this respect, it not only concerns the individual components of the address but also the name ele-ments. This fact is subsequently considered with a few examples.

Since there is an obligation to provide verification of compliance with the EU anti-terrorism regulation, it is advisable to guarantee that documentary evi-dence of matching is archived, so that the process characteristic “transparency” is provided.

FINALLY, THE DATA QUALITY NOT ONLY CONCERNS THE HIGH QUALITY OF THE IN-HOUSE MASTER DATA BUT ALSO THE QUALITY OF THE PROCESSES



Implementation of the matchingSeveral challenges have to be overcome before matching of the company data against the sanction lists can be successful.

These challenges relate to (a) the respective processes and (b) an appropriate technical solution for regular matching of the company data against the sanction lists which complies with the requirements of the EU anti-terrorism regulations.

– The permanent, routine validation of all the addresses kept in a company against the sanc-tion lists should therefore be guaranteed.

– Since company data normally concerns a larg-er volume of data, record-by-record matching is practically impossible. Solutions for batch processing are called for here.

– The various sanction lists have a heterogeneous structure. This means that the lack of stand-ardization of the different files must be solved technically. Ideally, the preparation of these sanction lists should be integrated in the actual calibration, so that the quality of the match-ing is not jeopardized by the heterogeneous structure of the sanction lists and their defective standardization.

Finally, a software solution is required which (a) reads in the various sanction lists without any problems and (b) is characterized by high, accu-rate match recognition.

Further characteristics of this software solution: It should be an intelligent expert system which uses address-specific analyses (phonetics, fuzzy logic) and country-based regulations. Individual param-etrability of the matching algorithms is also impor-tant, so that a high degree of flexibility is provided.

FINALLY, A SOFTWARE SOLUTION IS RE-QUIRED WHICH (A) READS IN THE VARI-OUS SANCTION LISTS WITHOUT ANY PROBLEMS AND (B) IS CHARACTERIZED BY HIGH, ACCURATE MATCH RECOGNITION.

http://www.uniserv.com/en/products/data-quality-solutions/data-cleansing-data-quality.php




USE SCENARIOS

When considering a conventional customer envi-ronment with typical processes, there are five areas in which validation against the sanction lists appears to be appropriate:

– Initial clean-up and database cleansing of the company data

– External lists (list rental/purchase)

– Individual processing for employees in the call center

– Electronic processing of single records

– Batch processing for authorized employees

INITIAL CLEAN-UP AND DATABASE CLEANSING OF THE

COMPANY DATA

The goal of an initial clean-up and routine data-base cleansing is the validation of all the persons, organizations and addresses in the system against the latest version of the sanction lists. The result of this validation is a „warning file“ which is further processed manually or electronically. All the poten-tial positive hits of the matching are shown in this warning file. The data records must be reviewed in every case. It is recommended that database cleansing is carried out regularly.

EXTERNAL LISTS (LIST RENTAL OR PURCHASE)

If address data is acquired from external sources, either by rental or purchase, these addresses must also be validated against the sanction lists. Each list should be checked before it is used. Suspicious cases should not be available for further process-ing, irrespective of the downstream processes. Examples of downstream processes include the transfer of these addresses to the in-house system or their use in advertising activities.

INDIVIDUAL PROCESSING FOR EMPLOYEES IN THE

CALL CENTER

Whereas company data and external address lists can be validated against sanction lists in batch proc-esses, the data is validated record-by-record in call centers. Here, for example, the software validates each new address to check whether it concerns a per-son on the sanction lists. If there are grounds for suspi-cion, the administrative employee receives a message that the address cannot be entered in the system at this point. The address is then made available for separate processing. In the case of the electronic processing of single records, the suspect addresses of firm matches against the sanction lists are marked fully automatically and/or integration in the system prevented.

ELECTRONIC PROCESSING OF SINGLE RECORDS

Automatic matching, which is not actively perceived, is executed in thebackground during data accept-ance. Potential matches are marked in thisprocess, so that they can be processed by authorized personnel at alater stage (see below). The advantage of this application scenario isthat employees are not irritated by possible warnings during customercontact and can continue to follow their respective processes.

BATCH PROCESSING FOR AUTHORIZED EMPLOYEES

The batch processing of the suspicious cases can be entrusted to specially qualified employees in the company. Here the suspicious cases from the data-base cleansing and the validation of external lists are loaded into the system and submitted to the employ-ees for their decision. A real-time tool is used for the actual decision; this carries out the same validations for the suspicious cases as for the batch processes but displays them individually on the screen record-by-record. It is thereby guaranteed that the results of each validation from the batch runs are manually validated and ruled upon.



As already mentioned, a high quality of the com-pany data is one of the prerequisites for successful matching against the sanction lists. Another pre-requisite is naturally the availability of the actual sanction lists. There are two sources of supply for German address holders:

– A) Download of the EU sanction lists directly from the Internet (http://www.un.org/sc/committees/1267/consolist.shtml)

– B) HADDEX sanction lists from the Federal Gazette (subject to a fee, include US lists in addition to the EU sanction lists) (http://www.awr-portal.de)

The EU sanction lists must be considered in a matching process. US lists are optional.

EXAMPLES OF ADDRESSES IN THE SANCTION LISTS

A few examples from the sanction lists are shown in the following, in order to illustrate the particular challenges for a software solution. All the examples are original entries from the HADDEX sanction lists.

Example 1:

Prerequisites for matching against sanction lists

Name:

Country/Place:

Al-Shifa Honey Press For Industry And Commerce

YEMEN/Aden

YEMEN/Sanaa

YEMEN/Taiz

QATAR/Doha

or

or

or

Address:

By the Shrine Next to the Gas Station

WHEN COMPARING THE THREE EXAMPLES, IT BECOMES CLEAR WHY NO COUNTRIES ARE STATED IN THE SANCTION LISTS; THE MAIN FOCUS IS ON ORGANIZATIONS AND INDIVIDUALS WHO CAN OPERATE FROM DIFFERENT COUNTRIES.


http://www.un.org/sc/committees/1267/consolist.shtml

http://www.un.org/sc/committees/1267/consolist.shtml

http://www.awr-portal.de

http://www.uniserv.com/de/solutions/compliance.php



Example 2: Example 3:

When comparing the three examples, it becomes clear why no countries are stated in the sanction lists; the main focus is on organizations and indi-viduals who can operate from different countries.

London GB NW8 Park Road 129

Name:

or

or

Fazul Abdullah Mohammed

ABDALLA, Fazul

25.8.1972

5.12.1974

25.2.1974

Moroni, Comoros

Citizen of the Comoros or citizen of Kenya

or

Date of birth:

Place of birth:

Info:

Name:

Abou El Baraa

or

Aiadi Ben Muhammad

or

Ayadi Chafik

or

Ben Muhammad

or

Ben Mohamed Ayadi Chafig

or

Bosnian pass 0841438 issued on 30.12.98 exp. on 30.12.03.

or

Mouscron BE Chaussee De Lille 2830.12.03.

or

München DE 80809 Helene Meyer Ring 10-1415

E423362 deliv. in Islamabad on 15.5.88

1292931

Pass:

Nat.ID:

Addresses:



A further challenge is the procedure for missing elements. In this respect, a complete address is not stated in most cases. In many cases, there is only a place name, or only the name elements are known.

The software must also be able to deal with single letters, abbreviations, acronyms and numbers (not for private individuals). The same applies to the handling of synonyms, names of organizations in various national languages and the processing of Unicode data.

For all these requirements, it must be guaranteed that as many correct matches are obtained as pos-sible. The recognition of phonetic differences and similarities must be ensured. Furthermore, there is a high probability of recording errors, particularly in the case of foreign names. The software must also be able to deal with such artefacts.

Finally, an optimum balance between the require-ments of the EU anti-terrorism regulation and the options for manual review must be found.

The requirements for a suitable software can be deduced from the above examples. First of all, the software must be able to deal with „mixed stock“. In the context of company data, this is understood to be a mixture of individuals, organizations, com-panies and institutions.

Special features of the fields of the sanction lists must also be considered. There are frequently a number of name elements which are difficult to identify as first names or last names (in the case of private individuals). In addition, there are name elements from little known cultural areas and lan-guage families (Arabic or Asian names).

The handling of country-specific entries and „embargo-typical“ entries such as Arabic first names, titles, name prefixes must also be pro-vided by the software solution.

Requirements for the software

FINALLY, AN OPTIMUM BALANCE BET-WEEN THE REQUIREMENTS OF THE EU ANTI-TERRORISM REGULATION AND THE OPTIONS FOR MANUAL REVIEW MUST BE FOUND.



EXAMPLES OF MATCHING ALGORITHMS

Here are some more examples to illustrate the requirements for the software. As above, all the examples are original entries from the HADDEX sanction lists.

BATCH VERSUS REAL-TIME PROCESSING

Depending on the type of processing, there are various requirements for the batch software or a real-time solution for record-by-record processing.High performance of the software and the simple setting of individual parameters play a large role in batch processing.Queries against the sanction lists must not become a bottleneck (time, performance!) in record-by-record processing with a real-time solution. Load distribution should be provided, e.g. via redundant server processes, in the case of large numbers of addresses or a large number of individual queries at the same time.

= is equal to = is not equal to

Acronyms

Afghan Support Committee

ASC Al Qaida au Maghreb islamique AQMI

AQMI

Synonyms

Grupo Salafista de Prédica e Combate

ASC Al Qaida au Maghreb islamiqueSalafist group for Call and Combat

Single letters

Al Baraka Exchange L. L. C.

ASC Al Qaida au Maghreb islamiqueL. C. Miller

Correspondence of individual elements

Abu Bakr Al-Jaziri

ASC Al Qaida au Maghreb islamiqueYasin Al-Qadi

Overhangs / Order

EL KHABIR, Abu Hafs el Masry

ABU HAFS

Djamel Mustafa

Mustafa

Djamel Mustafa

Mustafa Djamel

Numbers in name elements

H7

Hadamie

H7

H97


http://www.uniserv.com/en/products/data-quality-solutions/real-time-data-quality.php



Example 1:

Al-Shifa Honey Press For Industry And Commerce

Corres pond-ence

Record_ID Name

100 000370000965 Al-Shifa Honey Press For Industry And Commerce

Example 2:

osama bin ladin

Corres pond-ence

Record_ID Name

90 000329001792 Osama bin Laden-Network

90 000329001793 Osama bin Laden-Organi-sation

75 000833005781 Usáma bin Ládin

70 000329000906 Usama Bin Laden Network

70 000833005772 Bin Laden Usama

Example 3:

Vladimir Abramovich Kerler

Corres pond-ence

Record_ID Name

81 002290004108 Kerler Vladamir Abramovich

The processes following matching of the company data against the sanction lists are just as important as the matching process itself and compliance with the data quality requirements.If suspicious cases were found during matching, these cases must be manually checked in every case. The software must provide appropriate lists and / or files with the data records for this purpose.It also is important that the matching results are incor-porated in the company data in such a way that the matches are documented. Matches in the data should therefore be marked. Furthermore, statutory data pursu-ant to Article 70 of the AWG must be passed on to the respective authorities. Documentation of the validation runs is just as impor-tant. Puschke & Hohmann (2008: 34) recommend that appropriate documentary evidence of the valida-tion runs is stored for a period of three to four years.

EXAMPLES OF POSITIVE MATCHES

Three examples are provided to illustrate the possible form of positive matches. The first data record comes from the sanction lists. The subsequent data records could be from the company data. The numbers under „Correspondence“ represent the degree of similarity and accuracy of the match. The range here is between 70 and 100. The record ID represents an individual number of the data record.

Downstream processes



No

COMPANY DATA COMPANY DATA

SANCTION LISTS

Validation evidence

Matching against Sanction lists

Suspicion

Yes

GOOD GUYS

BAD GUY?

Info to BAFA

COMPLETE MATCHING OPERATION

Example of a complete matching operationThe complete matching process therefore consists of the actual matching, the generation of appropriate validation records, the generation of a list of the suspicious cases, the marking of the suspicious cases and the non-suspicious cases in the database, the manual checking of the suspicious cases and the reporting of the individuals and organizations identified as suspicious to the BAFA:

BAD GUYS

Manual check of the results evidence



Your first step towards implementation of the EU anti-terrorism regulation is quite simple: Contact us! The link will take you directly to the contact centre of Uniserv. We look forward to hearing from you and will be pleased to advise you and help you in your project.

List of references

– Puschke, M. (publisher) & Hohmann, H., 2008. Basiswissen Sanktionslisten. Hintergrund und Praxis der Integration von

Sanktionslisten in Ihre Geschäftsprozesse. [Basic knowledge of sanction lists. The background and practice for the integra-

tion of sanction lists in your business processes.] Bundesanzeiger Verlagsges. mbH, Cologne. 149pp.

– http://bundesrecht.juris.de/owig_1968/__130.html

– http://dejure.org/gesetze/StGB/14.html

– http://www.gesetze-im-internet.de/awg/__34.html

– http://www.zoll.de/e0_downloads/a1_vorschriften/a0_gesamtliste_gesetze/aussenwirtschaftsverordnung.pdf

– Rohweder, J.P., Kasten, G., Malzahn, D,. Piro, A., Schmid, J. 2008. Informationsqualität - Definitionen, Dimensionen und

Begriffe. In: Hildebrand, K., Gebauer, M. Hinrichs, H. Mielke, M. (Hrsg.) Daten- und Informationsqualität. Auf dem Weg

zur Information Excellence. Vieweg & Teubner. S. 25-45.

Exclusion of liability: The contents of this White Paper were prepared with the greatest of care. However, we are unable to

guarantee that the contents are correct, complete or up-to-date.

How to contact usThe Uniserv DQ Audit presents itself, in order to be able to make statements about the status quo of the in-house data in the Compliance system.

For further informationplease visit our web page www.uniserv.com or contact us directly:

We are looking forward for advising and sup-porting you through your project.

http://bundesrecht.juris.de/owig_1968/__130.html

http://dejure.org/gesetze/StGB/14.html

http://www.gesetze-im-internet.de/awg/__34.html

http://www.zoll.de/e0_downloads/a1_vorschriften/a0_gesamtliste_gesetze/aussenwirtschaftsverordnung.pdf


mailto:[email protected]



UniservUniserv is the largest specialised supplier of data quality solutions in Europe with an internationally usable software portfolio and services for the quality as-surance of data in business intelligence, CRM applications, data warehousing, eBusiness and direct and database marketing.

With several thousand installations worldwide, Uniserv supports hundreds of customers in their endeavours to map the Single View of Customer in their customer data-base. Uniserv employs more than 110 people at its head-quarters in Pforzheim and its subsidiary in Paris, France, and serves a large number of prestigious customers in all sectors of industry and commerce, such as ADAC, Al-lianz, BMW, Commerzbank, DBV Winterthur, Deutsche Bank, Deutsche Börse Group, France Telecom, Green-peace, GEZ, Heineken, Johnson & Johnson, Nestlé, Payback, PSA Peugeot Citroën as well as Time Life and Union Investment.

Further information is available at www.uniserv.com

Experience: OVER 40 YEARS

Market position:LARGESTEUROPEAN SUPPLIER

Employees: MORE THAN 110 PEOPLE

DIRECT MARKETING

BI/BDW

CPM

CRM

ERP

E-COMMERCE

DATA MIGRATION

PROJECTS

SOA

ON-PREMISE/ON-DEMAND

MDM/CDI

COMPLIANCE

Contact:+49 7231 936-1000

UNISERV GmbH Rastatter Straße 13 • 75179 Pforzheim • Germany • T +49 7231 936-0 • F +49 7231 936-3002 • E [email protected] • www.uniserv.com© Copyright Uniserv • Pforzheim/Germany • All rights reserved.


data quality and compliance

Business

compliance page

current sanction lists

various sanction lists

white paper sanction

sanction listsrequirements

eu antiterrorism regulation

prospect data

data qualityimplementation