F.A.Q.
Frequently asked questions
What news sources does the solution work with?
Connectors for the following media providers are available out of the box:
- Google API
- Dow Jones Factiva (Headlines)
- LexisNexis L&P News v1
- World-Check One
In addition, users have the option to import data for an entity directly using a CSV file.
I don’t see the media provider I want to work with. Is it possible to add a connection for them?
Typically, integrating with a new media provider is easy. However, it depends on the provider’s capabilities and business requirements.
Additional time may be required for establishing a partnership agreement or for vendor approval processes. Precise business requirements are key to expediting the process.
Can I post AMM results back to a data provider?
Posting results back to LexisNexis is not supported out of the box. Professional services would need to customize this.
Can I configure where the AMM results are stored?
The final results in the format of a report are stored in S3. It is also possible to send the final report via email.
Storing in a different location requires customization.
What languages are supported?
The model within the AI Agent natively understands both English and Spanish articles. Additional languages are not supported now but can be discussed as a future enhancement. In addition, the entire Business Process, Manual Tasks, and the final report can be localized to either English or Spanish.
You also have the option to dictate the language of the returned articles. This is partially dependent on what each provider allows.
The article language options for Google are listed below. This list can be expanded if required.
- English
- Spanish
- English & Spanish
- Any
Factiva Headlines and LexisNexis currently allow you to choose English or Any.
World-Check One only supports English at this time.
Can the solution automatically translate the articles and present the article in the language specified by the user?
The Adverse Media Monitoring solution supports translating, adjudicating, and dispositioning articles in English and Spanish. Additional languages require customization.
Can the output report be modified?
Customers can receive the report in the PDF or HTML format as part of the packaged AMM solution. Additional modifications to the report require customization.
The articles from Google displayed in WorkFusion are not the same as the search results retrieved manually using Google. Why is that?
There are three common reasons a comparison of searches (manual vs. WorkFusion) may produce different results:
The searches are not exactly the same. You conduct them with different parameters or media providers.
WorkFusion automatically filters out results that are located behind a paywall or not credible (Facebook comments, tweets from Twitter, and so on). This list is compiled based on the best practices from industry consultants and the processes in place at top 50 banks. However, it can be customized based on the individual customer needs.
For customers using Google, the results are ranked based on Google’s internal algorithm that is optimized for advertising. The AMM solution from WorkFusion ranks the articles based on their relevance and risk for an AML compliance violation. The order is different when you look at the results after WorkFusion has completed its analysis.
Does the solution filter out duplicate articles?
Google API, Dow Jones Factiva Headlines, LexisNexis Bridger, and World-Check One have duplicate checks turned on by default. Duplicate articles are shown as a single article for analysis when those media providers are used.
Articles sourced from other media providers that do not offer a duplicate check as part of their service will not be flagged or skipped within WorkFusion’s solution.
How is an age mismatch determined?
When the year of birth is provided for an entity, the AMM solution will calculate the age automatically and use it for comparison. This age is buffered by 24 months to account for differences with regard to the month. As each article is reviewed, the machine learning model considers the year the article was published, the age listed in the article, and the year of birth provided for the entity.
For example:
An entity, “Jane Doe,” was born in 1980 and is being screened in 2022. She has a calculated age of 42. An article published in 2010 that showed “Jane Doe, age 42” would be automatically dispositioned as False Positive because the entity being screened was 32 that year.
An entity, “John Smith,” was born in 1980 and was being screened in 2022. He has a calculated age of 42. An article published in 2020 that shows “John Smith, age 41,” would be escalated for additional review because the model calculated that he would have been 40 in 2020. Due to birth months and age, the model erred on caution as the age is within the 24-month buffer.
How does Evan (formerly Evelyn AMM) handle continuous monitoring for duplicates (customers are hesitant to pay for the same search over and over within a specified window)?
In the current version, you can either schedule Adverse Media Screenings for customers (whether new or existing) or run investigations on the ad-hoc basis. There is no check within the AI Agent to flag or skip previously screened entities within a given time frame. However, it is available as a customization. There are plans to add this as part of the packaged solution by the end of 2022.
How are ad-hoc searches executed within the platform?
To perform an ad-hoc search, you must first run the Ad hoc Investigation Business Process from Control Tower. This Business Process will launch an ad-hoc search task within Workspace.
To use the search, users can either hand key in the information via the interface or upload a CSV file with the entities they want to investigate.
Note that selecting specific parameters, such as the Search Provider or Article Language, is determined by the parameters set when installing the AI Digital Worker as they require additional licenses, keys, and permissions.
Can Google news searches be executed to include multiple languages in a single process? For example, can the AI Digital Worker read English and Spanish articles without having to run the process twice?
This functionality is supported in version AMM 3.0 and higher. Customers on earlier releases must run the process twice: once to disposition English articles and once to disposition Spanish articles.
When using the Google API, why do some articles only show with the preview text?
There are a few reasons why this may occur:
Currently, the model will review, prioritize, and adjudicate articles based on their relevance to AML-related crimes. The majority of articles are available in the HTML format. However, any content not available in HTML (such as a PDF, Excel, XML file, and so on) will not be rendered within the Workspace application. Instead, a source link is provided to the user to navigate directly to the source and review the content in a separate browser tab. Note that if it is a file, the analyst may be prompted to download it instead.
Google API sources articles from prominent news providers that may require subscriptions to access their content. If this occurs, the AI Agent will pass the article along, but the content cannot be downloaded as it is hidden behind a paywall. Users should click the link to navigate the article and review it manually.
Google API sources website articles that follow varying maintenance schedules. An investigation may occasionally run during a scheduled (or unexpected) downtime. When this occurs, the preview text from Google can be extracted and rendered (the preview text you typically see when manually screening an entity). The entire content of the article cannot be extracted and, therefore, cannot be rendered within the UI. A link to the source is provided for the user to navigate directly to the source and review the content in a separate browser tab. Note that if it is a file, the analyst may be prompted to download it instead.
Some websites need to use better practices when creating a content schema. While the AI Digital Worker has spent a few years learning various unusual schemas, the Internet changes rapidly, and a new schema that isn't easy to parse may be encountered. If this occurs, reach out with a link to the article, and the team will add the required logic to handle these.
Why do some articles have a high score and a status of False Positive?
The ML model leverages natural language processing, historical data, and customizable risk factors when determining the likelihood that an article indicates a risk for the entity being screened. Once risk is assessed, and the score is generated, it will determine if additional information can be leveraged to eliminate the article as a False Positive. For example, an article for an entity named Theodore Bundy may return a few articles for the infamous criminal Ted Bundy. However, the particular Theodore Bundy being screened was born in 1995, after the crimes were committed. In this instance, those articles would be flagged as high risk but then correctly dispositioned by the model as False Positive due to an age mismatch. Note that within the Manual Task and the final report, a human-readable explanation is always provided for decisions made by the model.
How does Evan handle partial names?
The ML model includes Name Entity Recognition (NER). NER is used to find full names, partial names, and common nicknames (Mikhail and Misha, William and Bill, and so on).
Names and partial names are included in the classification decision and highlighted within the Manual Task and Report text.
How does changing the Search Period affect the search results?
The Search Period filters out results that do not fit within the specified time.
It is important to remember that sometimes new articles are written about older crimes. This means that if you set the period to 30 days, it is possible to receive an article published yesterday in the results for a crime committed ten years ago.
Why can’t I change the Search Period for World-Check One?
World-Check One does not support this.