Input and output data

Input entity data

Input data relates to the individual or organization being screened for sanctions or existence on Public Exposed Persons (PEP) lists. The Business Process uses this data as input for screening.

Various screening systems may need different fields. For example, Dow Jones only requires value from input_full_name.

Remember to provide the data as a CSV file.

info

Note that the Model only works if your input data includes mandatory parameters. See the corresponding column in the table below.

Field	Description	Example
input_id	Customer record ID	1111
input_record_type	Record type: Person / P / Individual / I Entity / Organization / O / Company / C Security / S Any other string is treated as Unknown	Person
input_gender	Gender of the Individual type in the record: Male / Man / M Female / F / Woman / W Any other strings is treated as Unknown	MALE
input_full_name	Full name of the screened record	Kasim Rimsevi
input_alias_name*	A.K.A name or alias name of the record	Kasim Ibragim Rimseiv
input_date_{i} input_date_type_{i}	Any date related to the screening record: input_date_{i} should contain the actual date in the MM/DD/YYYY format input_date_type_{i} should contain the corresponding date type	10/26/1981 (MM/DD/YYYY) DOB
input_address_{i}_country input_address_{i}_state input_address_{i}_city input_address_{i}_type	Any address information related to the screened record

* You can add any number of available names if more than two names use an index or have a unique middle portion of the field, for example:

• input_alias_name, input_aka_name, input_synonym_name
• input_alias1_name, input_alias2_name, …

Watch List Entity Data

Watch List Entity Data comes from screening systems like Doe Jones, World Check One, and so on.

Field	Description	Example
hit_id	ID of the sanctioned, high-risk, or politically exposed individual alert.	1362545
hit_record_type	Record type of the sanctioned, high-risk, or politically exposed entity.	Person
hit_gender	Gender of the sanctioned, high-risk, or politically exposed individual.	Male
hit_matched_name	The partial name from a sanctions or PEP list detected as a potential match by the screening software of the sanctioned, high-risk, or politically exposed entity.	Qasim Al-Rimi
hit_primary_name*	Name of the sanctioned, high-risk, or politically exposed entity.	Qasim Al-Rimi
hit_date_{i} hit_date_type_{i}	Date information of a Hit Entity. Usually DOB (date of birth) or DOR (date of registration). It can be a partial date.	06/15/1978, 06/14/1978 (MM/DD/YYYY), 09/1994, 1944
hit_address_{i}_country hit_address_{i}_state hit_address_{i}_city hit_address_{i}_type	Address information of a Hit Entity.	06/15/1990, 09/?/1994, ?/?/1944

* NSS parses and uses any possible names provided by screening systems.

Model Decision Data per Hitted Entity

The Name Screening skill matches inputs and tries to ascertain whether the generated hit is indeed a true hit, one of the many generated false hits, or if the provided information is insufficient for the decision. The matching logic inside the process informs the output fields.

Field	Description
resolution_input_decision	Overall decision for full Alert or Case. No hits: there are no Hit Entities for the screened record. Needs further review: at least one Hit entity is marked as Need more information. Cleared Alert: all Hit entities are marked as False positive.
resolution_input_details	Details on the status explanation. Contains data about the number of hits marked as False positive or Need more information.
resolution_hit_decision	ML Model decision. 0: False positive 1: Need more information
resolution_hit_confidence	ML Model's confidence in the decision taken.
resolution_hit_explanation	Human readable explanation of the Model's decision.
resolution_hit_field_type	ML Model's decision on the record type field: 0: mismatch 1: match 2: not enough information
resolution_hit_field_name	ML Model's decision on the name field, name matching similarity: Possible values from 0..1, where 1: full match 0: mismatch
resolution_hit_field_gender	ML Model's decision on the gender field: 0 - mismatch 1: match 2: not enough information
resolution_hit_field_date	ML Model's decision on the date field: 0: mismatch 1: match 2: not enough information
resolution_hit_field_address	ML Model's decision on the address field: 0: mismatch 1: match 2: not enough information

Quality Check report

Each Business Process execution produces a Quality Check report containing information for further human review or reconciliation purposes. The report is saved on the MinIO S3 site under the following path: doc-upload/name-sanctions-screening/QC_Reports/{year}/{month}/ QC_Names-Screening-<<yyyy-MM-dd_HH-MM-SS>>.csv. By default, you might not have permission to access this S3 bucket. To access it, create a Support ticket.

A Quality Check report contains input as customer information, hit data found in the screening system, and records specific to the screening system. The timestamp on the report name refers to the time the BP was run.

The Quality Check report contains the following columns:

Input Data columns
input_id	Firco Trust: system_id World-Check One: case_system_id DJ / CSV: input_id submitted in the input file
input_full_name	Firco Trust: the first name in the Customer Information names World-Check One / DJ / CSV: input_full_name submitted in the input file
input_country	Aggregated information containing all submitted addresses. Format: Type1 Country1 State1 City1 Street1 | Type2 Country2 State2 City2 Street2 Example: Citizenship GBR London | Jurisdiction GBR | Resident of GBR
input_gender	Gender of the Individual type in the record. Otherwise, empty.
input_type	Type of the record. Otherwise, empty.
Firco Trust CMAPI specific columns
firco_bussiness_unit	Firco Trust Alert Business Unit value.
firco_unit	Firco Trust Alert Unit value.
firco_system_id	Firco Trust Alert system_id value.
firco_message_id	Firco Trust Alert message_id value.
record_matching_string	Firco Trust Hit Entity record_matching_string value.
list_matching_string	Firco Trust Hit Entity list_matching_string value.
World Check-One specific columns
case_system_id	World Check-One Case system_id value.
group_id	World Check-One group_id.
Historical Data specific columns
hit_gold_resolution
Model resolution columns
model_desicion	ML Model's decision.
model_explanation	ML Model's explanation.
alert_desicion	Overall decision for entire Alert or Case.
alert_explanation	Details on the status explanation. Contains data about the number of hits marked as False positive or Need more information.
desicion_score	ML Model's decision score.
Additional information columns
auto_qc	Yes/No flag indicating that a hit is selected for auto QC.
input_names	Aggregated information containing all submitted screening names.
hit_names	Aggregated information containing all found WLE names.

The NSS Quality Check Report looks similar to this example.