Official Sanctions, Criminal, and Terrorist Watchlists

Compiled by various national authorities and international organizations including the United Nations,
the European Union, US OFAC, among others, these lists assist in identifying individuals and entities involved in illicit activities or those posing threats to global security.

• Islamic State - designated as a terrorist organization by multiple countries.
• Nicolas Maduro - Venezuelan President, sanctioned by the US and other countries.
• Oleg Deripaska - Russian oligarch sanctioned by the US and other countries.

Politically Exposed Persons (PEPs) Registers

These registers contain information about PEPs, which include politicians, senior military officers, executives of state-owned enterprises, along with their family members and close associates. Data is collected from official national government websites and open databases like Wikidata.

Some examples of PEP reports:

• Abdel Fattah el-Sisi - President of Egypt.
• Carsten Linnemann - German Bundestag member.
• Aziz Akhannouch - Prime Minister of Marocco.

Open Business Registers

We pull data from open business registers across various countries. This information, offering insight into corporate structures, ownership, and relationships, is crucial for understanding potential risks associated with businesses and their connections.

Unofficial Sanction Recommendations

Public organizations investigating corruption or other types of crime may offer unofficial lists of individuals or entities suggested for sanctions. These lists provide additional insights into potential risks not yet recognized by official channels. For example, ACF/FBK list of bribetakers and warmongers.

Offshore Leaks Databases

We harness information from offshore leak databases, including the Panama Papers and the Pandora Papers. These datasets expose hidden financial connections and potentially unlawful activities conducted through offshore jurisdictions, thereby enhancing our ability to detect and mitigate financial crimes and money laundering.

The Financial Action Task Force (FATF), an inter-governmental body aiming to combat money laundering, terrorist financing, and other related threats to the international financial system, has identified PEP involvement in financial transactions as a risk factor. DataSpike adheres to the FATF's PEP classifications:

Foreign PEPs: Individuals who hold or have held prominent public positions in a foreign country.

Domestic PEPs: Individuals who hold or have held prominent public positions within their own country.

International organization PEPs: Individuals who hold or have held senior positions within international organizations.

In line with the FATF's guidance, DataSpike aids financial institutions in implementing enhanced due diligence measures for dealing with PEPs to mitigate risks of money laundering and corruption.

In DataSpike, PEPs are further classified into high-risk, medium-risk, and low-risk positions, enabling a deeper understanding of their potential risk profile.

High Risk: Includes sanctioned entities, terrorists, and criminals involved in illegal activities.

Medium Risk: Includes PEPs, individuals with significant control over organizations, disqualified persons, entities in unofficial lists proposed for sanctioning, entities found in offshore leaks, and entities associated with public legal cases possibly related to financial crime, corruption, or other illicit activities.

Low Risk: Includes individuals or entities from public data sources that don't fall into the high or medium risk categories.

In DataSpike, each data source is tagged to denote what it pertains to. An AML report for a specific entity displays tags based on the data sources where the entity was found. Our current tag categories include:

1. Finance: This tag pertains to entities affiliated with the financial sector. These may encompass public figures engaged in financial institutions, those subject to financial restrictions, and organizations such as banks, hedge funds, insurance companies, and the like.
2. Legal: This tag refers to entities associated with legal issues or activities. It could indicate involvement in lawsuits, litigation, or other legal matters.
3. Terrorism: If this tag is present, the entity is found in databases that track terrorist individuals or organizations, or they are associated with terrorist activities.
4. Criminal: This tag is assigned to entities that are either involved in criminal activities or are listed in criminal databases.
5. PEP: This tag is used for Politically Exposed Persons. These are individuals who are or have been entrusted with prominent public functions, along with their immediate family members and close associates.
6. Media: This tag denotes entities that have been mentioned or featured in the media. It can provide additional context about an entity's public perception and activities.
7. Social: Entities associated with social data sources, like social media platforms or social network data, are assigned this tag.
8. Sanctions: This tag is used for entities that are on sanction lists. These are individuals or organizations that have restrictions imposed on them due to various reasons, such as involvement in illegal activities.
9. Unofficial Source: This tag pertains to entities listed in unofficial sources or databases. These may include lists or databases not recognized by official authorities but might still provide valuable insights.
10. Leaks: This tag is used for entities that appear in leaked documents or databases, such as the Panama Papers or the Pandora Papers. It usually indicates potential offshore or hidden activities.
11. Company Registry: Entities found in official company registries are tagged with this. It can help understand an entity's business affiliations and operations.
12. Debarred Entity: Entities that have been barred or excluded from conducting certain activities, often due to regulatory violations or misconduct. These entities may be individuals or businesses disallowed from participating in certain business operations or sectors.

At DataSpike, our commitment to comprehensive AML screening is second to none. As part of this commitment, we have implemented a sophisticated media monitoring system to identify and assess relevant news items pertaining to potential money laundering and financial crime activities.

Expansive News Sources

We continually analyze more than 10,000 news sources from around the globe. This broad range of sources ensures that our media coverage is extensive, helping us stay abreast of emerging trends, events, and risks that could impact AML efforts.

Innovative Machine Learning Techniques

To manage this vast amount of news data, we employ state-of-the-art machine learning techniques. These include named entity recognition, text classification, and sentiment analysis. Named entity recognition helps us identify and categorize specific entities that are mentioned in the news, such as individuals, companies, or locations. Text classification assists in understanding the context and content of news items, and sentiment analysis helps discern the tone and implications of a news story.

Adverse News Filtering

Our machine learning-driven system is specifically designed to filter out irrelevant news and hone in on adverse news relevant to AML efforts. This focus allows us to swiftly identify news items that may indicate potential risk behaviors or activities.

Through our media monitoring process, we can capture and analyze a wide array of information rapidly and accurately, providing valuable insights to our users. Our aim is to ensure that our AML screening solution remains responsive, robust, and reliable, capable of adapting to the ever-evolving landscape of financial crime.