Make Case
Performing a report search with AML Watcher is both intuitive and highly adaptable. It all starts with the core filter: the Name. From there, you have the flexibility to enrich your search by layering on additional filters, including Countries, Categories, Birth/Incorporation Date, Unique Identifier, and Match Score. These filters offer extensive customization, allowing you to fine-tune your search criteria precisely to your needs. What's more, these filters are fully combinable, giving you the power to refine your search with pinpoint accuracy. By default, no filters are applied, ensuring that you have the freedom to tailor your AML search process exactly as you require.
Endpoint: https://api.amlwatcher.com/api/search
Method: POST
- HTTP
- Javascript
- PHP
- Python
- Ruby
- Java
- cURL
- C#
- Go
Sample Request
POST /api/search HTTP/1.1
Host: api.amlwatcher.com
Authorization: Bearer Token
Content-Type: application/json
Content-Length: 573
{
"name": "Kim Campbell",
"birth_incorporation_date": "",
"categories": [],
"unique_identifier": "",
"countries": [],
"entity_type": [],
"exact_search": true,
"match_score": 100,
"group_data": false,
"alias_search": "false",
"rca_search": "false",
"ongoing_monitoring": "false",
"biometric_search_image": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACOoAAAL4CAYAAADP1oqyAAAMP2...",
"api_key": "Your Generated API Key",
"client_reference": "Jh88XGyN58X7EQ6BSquzeTfs",
"risk_score_engine_id": "65c3885cce288c0363bcfd4f",
"per_page": 15,
"webhook": "Provide webhook for Adverse Media"
"search_profile": ""
}
Sample Request
var myHeaders = new Headers();
myHeaders.append("Authorization", "Bearer Token");
myHeaders.append("Content-Type", "application/json");
var raw = JSON.stringify({
name: "",
categories: [],
countries: [],
birth_incorporation_date: "",
unique_identifier: "",
alias_search: true,
rca_search: true,
ongoing_monitoring: false,
biometric_search_image:
"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACOoAAAL4CAYAAADP1oqyAAAMP2...",
entity_type: ["Person"],
api_key: "Your Generated API Key",
client_reference: "Jh88XGyN58X7EQ6BSquzeTfs",
risk_score_engine_id: "65c3885cce288c0363bcfd4f",
per_page: 15,
match_score: 75,
group_data: false,
webhook: "Provide webhook for Adverse Media",
search_profile: "",
});
var requestOptions = {
method: "POST",
headers: myHeaders,
body: raw,
redirect: "follow",
};
fetch("https://api.amlwatcher.com/api/search", requestOptions)
.then((response) => response.text())
.then((result) => console.log(result))
.catch((error) => console.log("error", error));
Sample Request
<?php
$curl = curl_init();
curl_setopt_array($curl, array(
CURLOPT_URL => 'https://api.amlwatcher.com/api/search',
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => '',
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 0,
CURLOPT_FOLLOWLOCATION => true,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => 'POST',
CURLOPT_POSTFIELDS =>'{
"name": "",
"categories": [],
"countries": [],
"birth_incorporation_date": "",
"unique_identifier":"",
"alias_search": true,
"rca_search": true,
"ongoing_monitoring": false,
"biometric_search_image": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACOoAAAL4CAYAAADP1oqyAAAMP2...",
"entity_type": ["Person"],
"api_key": "Your Generated API Key",
"client_reference": "Jh88XGyN58X7EQ6BSquzeTfs",
"risk_score_engine_id": "65c3885cce288c0363bcfd4f",
"per_page":15,
"match_score":75,
"group_data": false,
"webhook": "Provide webhook for Adverse Media"
"search_profile": ""
}',
CURLOPT_HTTPHEADER => array(
'Content-Type: application/json',
'Authorization: Bearer Token'
),
));
$response = curl_exec($curl);
curl_close($curl);
echo $response;
Sample Request
import requests
import json
url = "https://api.amlwatcher.com/api/search"
payload = json.dumps({
"name": "",
"categories": [],
"countries": [],
"birth_incorporation_date": "",
"unique_identifier": "",
"alias_search": True,
"rca_search": True,
"ongoing_monitoring": False,
"biometric_search_image": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACOoAAAL4CAYAAADP1oqyAAAMP2...",
"entity_type": ["Person"],
"api_key": "Your Generated API Key",
"client_reference": "Jh88XGyN58X7EQ6BSquzeTfs",
"risk_score_engine_id": "65c3885cce288c0363bcfd4f",
"per_page": 15,
"match_score": 75,
"group_data": False,
"webhook": "Provide webhook for Adverse Media"
"search_profile": ""
})
headers = {
'Authorization': 'Bearer Token',
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
Sample Request
require "uri"
require "json"
require "net/http"
url = URI("https://api.amlwatcher.com/api/search")
https = Net::HTTP.new(url.host, url.port)
https.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Content-Type"] = "application/json"
request['Authorization'] = "Bearer Token"
request.body = JSON.dump({
"name": "",
"categories": [],
"countries": [],
"birth_incorporation_date": "",
"unique_identifier": "",
"alias_search": true,
"rca_search": true,
"ongoing_monitoring": false,
"biometric_search_image": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACOoAAAL4CAYAAADP1oqyAAAMP2...",
"entity_type": ["Person"],
"api_key": "Your Generated API Key",
"client_reference": "Jh88XGyN58X7EQ6BSquzeTfs",
"risk_score_engine_id": "65c3885cce288c0363bcfd4f",
"per_page": 15,
"match_score": 75,
"group_data": false,
"webhook": "Provide webhook for Adverse Media"
"search_profile": ""
})
response = https.request(request)
puts response.read_body
Sample Request
import java.io.DataOutputStream;
import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.net.HttpURLConnection;
import java.net.URL;
public class Main {
public static void main(String[] args) {
HttpURLConnection connection = null;
DataOutputStream outputStream = null;
try {
// Define the URL
URL url = new URL("https://api.amlwatcher.com/api/search");
// Open a connection to the URL
connection = (HttpURLConnection) url.openConnection();
// Set the request method to POST
connection.setRequestMethod("POST");
// Set the request headers
connection.setRequestProperty("Content-Type", "application/json");
connection.setRequestProperty("Authorization", "Bearer Token");
// Enable input/output streams
connection.setDoOutput(true);
// Define the JSON request body
String jsonPayload = "{\n" +
" \"name\": \"\",\n" +
" \"categories\": [],\n" +
" \"countries\": [],\n" +
" \"birth_incorporation_date\": \"\",\n" +
" \"unique_identifier\":\"\",\n" +
" \"alias_search\": true,\n" +
" \"rca_search\": true,\n" +
" \"ongoing_monitoring\": false,\n" +
" \"biometric_search_image\": \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACOoAAAL4CAYAAADP1oqyAAAMP2...\",\n" +
" \"entity_type\": [\"Person\"],\n" +
" \"api_key\": \"Your Generated API Key\",\n" +
" \"client_reference\": \"Jh88XGyN58X7EQ6BSquzeTfs\",\n" +
" \"risk_score_engine_id\": \"65c3885cce288c0363bcfd4f\",\n" +
" \"per_page\": 15,\n" +
" \"match_score\": 75,\n" +
" \group_data\": false,\n" +
" \"webhook\": \"Provide webhook for Adverse Media\"\n" +
" \"search_profile\": \"\"\n" +
"}";
// Write the JSON request body
outputStream = new DataOutputStream(connection.getOutputStream());
outputStream.writeBytes(jsonPayload);
outputStream.flush();
// Get the response code
int responseCode = connection.getResponseCode();
System.out.println("Response Code: " + responseCode);
BufferedReader in;
if (responseCode >= 200 && responseCode < 300) {
in = new BufferedReader(new InputStreamReader(connection.getInputStream()));
} else {
in = new BufferedReader(new InputStreamReader(connection.getErrorStream()));
}
String inputLine;
StringBuilder response = new StringBuilder();
while ((inputLine = in.readLine()) != null) {
response.append(inputLine);
}
in.close();
System.out.println("Response: " + response.toString());
connection.disconnect();
} catch (Exception e) {
e.printStackTrace();
}
}
}
Sample Request
curl --location --request POST 'https://api.amlwatcher.com/api/search'
--header 'Content-Type: application/json'
--data '{
"name": "",
"categories": [],
"countries": [],
"birth_incorporation_date": "",
"unique_identifier":"",
"alias_search": true,
"rca_search": true,
"ongoing_monitoring": false,
"biometric_search_image": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACOoAAAL4CAYAAADP1oqyAAAMP2...",
"entity_type": ["Person"],
"api_key": "Your Generated API Key",
"client_reference": "Jh88XGyN58X7EQ6BSquzeTfs",
"risk_score_engine_id": "65c3885cce288c0363bcfd4f",
"per_page":15,
"match_score":75,
"group_data": false,
"webhook": "Provide webhook for adverse media"
"search_profile": ""
}'
Sample Request
using RestSharp;
using System;
using System.Threading.Tasks;
class Program
{
static async Task Main(string[] args)
{
var options = new RestClientOptions("https://api.amlwatcher.com")
{
ThrowOnAnyError = true,
Timeout = TimeSpan.FromMilliseconds(-1),
};
var client = new RestClient(options);
var request = new RestRequest("/api/search", Method.Post);
request.AddHeader("Content-Type", "application/json");
request.AddHeader("Authorization", "Bearer Token");
var body = new
{
name = "",
categories = new string[] { },
countries = new string[] { },
birth_incorporation_date = "",
unique_identifier = "",
alias_search = true,
rca_search = true,
ongoing_monitoring = false,
biometric_search_image = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACOoAAAL4CAYAAADP1oqyAAAMP2...",
entity_type = new string[] { "Person" },
api_key = "Your Generated API Key",
client_reference = "Jh88XGyN58X7EQ6BSquzeTfs",
risk_score_engine_id = "65c3885cce288c0363bcfd4f",
per_page = 15,
match_score = 75,
group_data = false,
webhook = "Provide webhook for Adverse Media"
search_profile = ""
};
request.AddJsonBody(body);
RestResponse response = await client.ExecuteAsync(request);
Console.WriteLine(response.Content);
}
}
Sample Request
package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.amlwatcher.com/api/search"
method := "POST"
payload := strings.NewReader(`{
"name": "",
"categories": [],
"countries": [],
"birth_incorporation_date": "",
"unique_identifier":"",
"alias_search": true,
"rca_search": true,
"ongoing_monitoring": false,
"biometric_search_image": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACOoAAAL4CAYAAADP1oqyAAAMP2...",
"entity_type": ["Person"],
"api_key": "Your Generated API Key",
"client_reference": "Jh88XGyN58X7EQ6BSquzeTfs",
"risk_score_engine_id": "65c3885cce288c0363bcfd4f",
"per_page":15,
"match_score":75,
"group_data": false,
"webhook": "Provide webhook for Adverse Media"
"search_profile": ""
}`)
client := &http.Client {
}
req, err := http.NewRequest(method, url, payload)
if err != nil {
fmt.Println(err)
return
}
req.Header.Add("Content-Type", "application/json")
req.Header.Add("Authorization", "Bearer Token")
res, err := client.Do(req)
if err != nil {
fmt.Println(err)
return
}
defer res.Body.Close()
body, err := ioutil.ReadAll(res.Body)
if err != nil {
fmt.Println(err)
return
}
fmt.Println(string(body))
}
Request
| Parameters | Required | Type | Description |
|---|---|---|---|
| name | Yes | String | The name of entity/business you want to search. Min: 2 character Max: 100 characters Constraint: Name should not consist entirely of special characters or numbers. |
| countries | No | Array | Array of countries based on which you want to filters reports. See Countries. Note: ISO 3166-1 alpha-2 country codes are supported. Example: ["CA", "IN"] |
| categories | Yes | Array | Array of categories based on which you want to filters reports. See Categories. Example: ["Adverse Media", "SIP"] |
| birth_incorporation_date | No | String | Date(DD-MM-YYYY) based on which you want to filters reports. Note: To perform year search you can use the format 00-00-1947 and vice versa for date, month or combination of the three. Example: 10-03-1947 | 10-03-0000 | 00-03-1947 | 00-00-1947 |
| unique_identifier | No | String | Unique identifier can be used to filter the search records. Min: 2 character Max: 50 characters Constraint: Unique identifier should not consist entirely of special characters. Note: It can be any key within the data object like Passport No, National ID number. Example: TR12345677. |
| alias_search | No | Boolean | Alias search is used to specify whether user want to perform search within aliases or not. Note: The default value of alias_search is True. Example: True. |
| rca_search | No | Boolean | RCA search is used to specify whether user want to perform search within rca or not. Description: RCA (Relatives and Close Associates) - Immediate family members or close social or professional contacts of a government or political official, or senior executive – meaning spouses, parents, siblings, children, and spouses’ parents and siblings. Note: The default value of rca_search is True. Example: True. |
| ongoing_monitoring | No | Boolean | Ongoing monitoring is used to monitor the cases for Ongoing AML. Note: The default value of ongoing_monitoring is False. Example: True. Disclaimer: Please note that any adverse media retrieved through our API is provided as-is at the time of request, and we do not offer continuous updates or ongoing monitoring of adverse media screening. |
| biometric_search_image | No | Base64 encoded | Biometric AML Screening: AML Watcher's innovative screening matrix features Name + Image + DOB + Unique Identifier which facilitates precise matching. Biometric AML serves the purpose of reducing the manual effort of MLROs and compliance officers by minimizing false positives as well as false negatives with image based search filtration. Note: Image size should not be grater than 5MBs. Only JPG and PNG image types are accepted. |
| entity_type | No | Array | Entity Type: AML screening serves the purpose of identifying individuals or entities listed in various AML databases. AML Watcher provides screening services of five types of entities i.e. Person, Company, Organization, Crypto_Wallet, Vessel and Aircraft. Example: ["Person"]. |
| match_score | No | Integer | The match score determines the level of similarity required between the search term and the matched terms. A value of 0 allows for the loosest match, while 100 represents the closest match. Note: It ranges from 0-100. By default value is 80. When the selected Category is "Adverse Media," the match score is not applicable, as Adverse Media results are always returned with a match score of 100. Example: 65 |
| exact_search | No | Boolean | Sets the match score to 100, disables all preprocessing (including prefixes, autocorrection, and translation), and performs the search based solely on the exact input parameters. Default: False. |
| group_data | No | Boolean | When False: The API will return all data in a single, consolidated summary. When True: The API will return data segmented by individual data sources, providing a detailed, source-wise breakdown. Note: The default value of group_data is False. Example: False |
| api_key | Optional | String | An API key is required to perform search, only if access token is not passed in authorization header as a bearer token. |
| per_page | Optional | Integer/String | The option to specify the number of results per page is not mandatory; it is up to the user to decide how many they want to retrieve in a single request. |
| client_reference | No | String | A unique client reference can be assigned to each search. |
| risk_score_engine_id | No | String | A risk score engine id should be used while making case. |
| webhook | Required | String | A webhook is required to perform search when Adverse Media category is selected, All adverse media response will be sent on the provided webhook after processing. |
| search_profile | Optional | String | When searching with a search profile, the search will be performed within the selected databases and sanctions lists. Search Profile parameter takes input the search profile ID available in backoffice. Note: When using search profile precedence will be given to Search profile in comparison to Categories. |
Response
| Parameters | Description |
|---|---|
| error | Whenever there is an error in your request, this param will have details of that error; otherwise it’ll remain empty. |
| status | The status field is set to either “SUCCESS” or “FAIL”, indicating that the API request resulted in a successful or failure/error condition respectively. |
| data | An array containing the actual response elements. |
info
When group_data = True: The API will return Grouped Response.
When group_data = False: The API will return Old Response.
- Grouped Response Recommended
- Old Response
Sample Response
{
"data": {
"adverse_media_status": "PENDING",
"client_reference": "",
"match_status": "Potential Match",
"case_status": "Passed",
"pagination": {
"current_page": 1,
"records_per_page": 160,
"total_pages": 1,
"total_records": 20
},
"results": [
{
"birth_incorporation_date": [
"1947-03-10"
],
"categories": [
"Businessperson",
"SIP",
"PEP"
],
"countries": [
"International",
"Canada"
],
"data": {
"additional_information": {
"twitter_handle": [
"akimcampbell"
]
},
"identification_documents": [],
"legal_notice": [
"The information contained in this report is derived from public sources such as Official Government Websites, Global Watchlists, Compliance Reports, Published Research Articles and News Sources. AML Watcher® is not the source of the data and is not responsible for the content of third party sources. AML Watcher® does not determine any positive or negative risks associated with the profiled entity. These decisions are solely determined by our clients as mandated by their applicable regulatory obligations."
],
"linked_entities": [
{
"relation": [
"spouse"
],
"details": [
"Canadian actor, playwright, and pianist"
],
"name": [
"Hershey Felder"
]
},
{
"relation": [
"spouse"
],
"details": [
"Canadian mathematician, chess master, chess writer, chess official (1925-2012)"
],
"name": [
"Nathan Divinsky"
]
}
],
"sanction_details": [],
"summary": {
"address": [],
"alias": [
"Kim Campbellová",
"Avril Phaedra Douglas Campbell",
"Кэмпбелл, Ким",
"Avril Phædra Douglas Campbell",
"کیم کمپبل",
"კიმ კემპბელი",
"キム・キャンベル",
"金·坎贝尔",
"Кім Кэмпбел",
"킴 캠벨",
"Ким Кембъл",
"Κιμ Κάμπελ",
"کم کیمبل",
"किम क्याम्पबेल",
"كيم كامبل",
"Kim Campbell",
"קים קמפבל",
"Քիմ Քեմփբել",
"Ким Кэмпбелл",
"Кім Кемпбелл",
"Ким Кэмбелл",
"Ким Кембел",
"Kim Kempbel"
],
"category": [
"PEP"
],
"country": [
"Canada"
],
"date_of_birth": [
"1947-03-10"
],
"date_of_death": [],
"description": [],
"designation": [],
"education": [
"Prince of Wales Secondary School",
"Peter A. Allard School of Law",
"University of British Columbia",
"The Royal Conservatory of Music",
"London School of Economics and Political Science"
],
"email": [],
"entity_type": [
"Person"
],
"first_name": [
"Avril"
],
"gender": [
"female"
],
"keywords": [
"National government"
],
"last_name": [
"Campbell"
],
"name": [
"Kim Campbell"
],
"nationality": [
"Canada"
],
"net_worth": [],
"notes": [
"19th Prime Minister of Canada in 1993"
],
"occupation": [
"Politician"
],
"phone": [],
"place_of_birth": [
"Port Alberni Canada",
"Port Alberni"
],
"place_of_death": [],
"political_party": [],
"position": [
"Prime Minister of Canada (1993-1993)",
"member of the House of Commons of Canada"
],
"position_occupancies": [
"member of the House of Commons of Canada"
],
"religion": [
"Anglican Church of Canada"
],
"suffix": [],
"title": [],
"website": [
"http://www.kimcampbell.com/"
]
}
},
"entity_types": [
"Entity",
"Person"
],
"id": "TZVsTJNDuSkerVtLPzxyDK",
"matched_alias": "",
"matched_names": [
{
"matched_name": "Kim Campbell",
"matching_fields": {},
"record_id": "TZVsTJNDuSkerVtLPzxyDK",
"score": "100",
"source_ids": [
"431699"
]
},
{
"matched_name": "Kim Campbell",
"matching_fields": {
"alias": false,
"birth_incorporation_date": true,
"image": false,
"profile_name": true
},
"record_id": "L8ayNgJBRH4FtzLfGstxLN",
"score": "100",
"source_ids": [
"17109391",
"431871"
]
}
],
"matched_rca": "",
"name": "Kim Campbell",
"relevance_status": {
"alias": false,
"birth_incorporation_date": false,
"category": true,
"country": false,
"entity_type": false,
"image_match": false,
"potential_match": false,
"profile_name": true,
"rca_name": false,
"unique_identifier": false
},
"risk_audit": {
"category_rules": {
"business": {
"absolute_score": 10,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"businessperson": {
"absolute_score": 10,
"weightage": 50,
"weighted_score": 5.0
},
"fitness and probity": {
"absolute_score": 80,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"insolvency": {
"absolute_score": 80,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"pep": {
"absolute_score": 70,
"weightage": 50,
"weighted_score": 35.0
},
"pep level 1": {
"absolute_score": 70,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"pep level 2": {
"absolute_score": 49,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"pep level 3": {
"absolute_score": 40,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"pep level 4": {
"absolute_score": 25,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"rca": {
"absolute_score": 49,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"sanctions": {
"absolute_score": 100,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"sie": {
"absolute_score": 90,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"sip": {
"absolute_score": 90,
"selected": true,
"weightage": 50,
"weighted_score": 45.0
},
"warnings and regulatory enforcement": {
"absolute_score": 90,
"selected": false,
"weightage": 50,
"weighted_score": 0
}
},
"country_rules": {
"afghanistan": {
"absolute_score": 75.71,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"albania": {
"absolute_score": 38.63,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
....
}
},
"risk_decision": "Failed",
"risk_level": "High",
"risk_score": 95,
"risk_score_engine_id": "65ce1f26a74c6232ed9ce828",
"risk_title": "AML Default Risk",
"risk_view": {
"categories": {
"risk_level": "Medium",
"risk_scores": {
"Businessperson": 10,
"PEP": 70,
"SIP": 90
},
"score": 45.0,
"weightage": 50
},
"countries": {
"risk_level": "Medium",
"risk_scores": {
"Canada": 25.41,
"International": 100
},
"score": 50.0,
"weightage": 50
},
"crimes": {
"risk_level": "Low",
"risk_scores": {},
"score": 0,
"weightage": 0
},
"custom_list": {
"risk_level": "Low",
"risk_scores": {},
"score": 0,
"weightage": 0
}
},
"source_details": [
{
"categories": [
"PEP",
"Businessperson",
"SIP"
],
"countries": [
"International"
] ,
"data": {
"additional_information": {
"twitter_handle": [
"akimcampbell"
]
},
"identification_documents": [],
"legal_notice": [
"The information contained in this report is derived from public sources such as Official Government Websites, Global Watchlists, Compliance Reports, Published Research Articles and News Sources. AML Watcher® is not the source of the data and is not responsible for the content of third party sources. AML Watcher® does not determine any positive or negative risks associated with the profiled entity. These decisions are solely determined by our clients as mandated by their applicable regulatory obligations."
],
"linked_entities": [
{
"relation": [
"spouse"
],
"details": [
"Canadian actor, playwright, and pianist"
],
"name": [
"Hershey Felder"
]
},
{
"relation": [
"spouse"
],
"details": [
"Canadian mathematician, chess master, chess writer, chess official (1925-2012)"
],
"name": [
"Nathan Divinsky"
]
}
],
"sanction_details": [],
"summary": {
"address": [],
"alias": [
"Kim Campbellová",
"Avril Phaedra Douglas Campbell",
"Кэмпбелл, Ким",
"Avril Phædra Douglas Campbell",
"کیم کمپبل",
"კიმ კემპბელი",
"キム・キャンベル",
"金·坎贝尔",
"Кім Кэмпбел",
"킴 캠벨",
"Ким Кембъл",
"Κιμ Κάμπελ",
"کم کیمبل",
"किम क्याम्पबेल",
"كيم كامبل",
"Kim Campbell",
"קים קמפבל",
"Քիմ Քեմփբել",
"Ким Кэмпбелл",
"Кім Кемпбелл",
"Ким Кэмбелл",
"Ким Кембел",
"Kim Kempbel"
],
"category": [
"PEP"
],
"country": [
"Canada"
],
"date_of_birth": [
"1947-03-10"
],
"date_of_death": [],
"description": [],
"designation": [],
"education": [
"Prince of Wales Secondary School",
"Peter A. Allard School of Law",
"University of British Columbia",
"The Royal Conservatory of Music",
"London School of Economics and Political Science"
],
"email": [],
"entity_type": [
"Person"
],
"first_name": [
"Avril"
],
"gender": [
"female"
],
"keywords": [
"National government"
],
"last_name": [
"Campbell"
],
"name": [
"Kim Campbell"
],
"nationality": [
"Canada"
],
"net_worth": [],
"notes": [
"19th Prime Minister of Canada in 1993"
],
"occupation": [
"Politician"
],
"phone": [],
"place_of_birth": [
"Port Alberni Canada",
"Port Alberni"
],
"place_of_death": [],
"political_party": [],
"position": [
"Prime Minister of Canada (1993-1993)",
"member of the House of Commons of Canada"
],
"position_occupancies": [
"member of the House of Commons of Canada"
],
"religion": [
"Anglican Church of Canada"
],
"suffix": [],
"title": [],
"website": [
"http://www.kimcampbell.com/"
]
}
},
"description": "Pantheon is project that uses biographical data to expose patterns of human collective memory. Pantheon contains data on more than 70k biographies, which Pantheon distributes through a powerful data visualization engine centered on locations, occupations, and biographies. Pantheon’s biographical data contains information on the age, occupation, place of birth, and place of death, of historical characters with a presence in more than 15 language editions of Wikipedia. Pantheon also uses real-time data from the Wikipedia API to show the dynamics of attention received by historical characters in different Wikipedia language editions.",
"publisher": "Pantheon World",
"url": "https://pantheon.world/"
},
{
"categories": [
"PEP"
],
"countries": [
"Canada"
],
"data": {
"additional_information": {
"twitter_handle": [
"akimcampbell"
]
},
"identification_documents": [],
"legal_notice": [
"The information contained in this report is derived from public sources such as Official Government Websites, Global Watchlists, Compliance Reports, Published Research Articles and News Sources. AML Watcher® is not the source of the data and is not responsible for the content of third party sources. AML Watcher® does not determine any positive or negative risks associated with the profiled entity. These decisions are solely determined by our clients as mandated by their applicable regulatory obligations."
],
"linked_entities": [
{
"relation": [
"spouse"
],
"details": [
"Canadian actor, playwright, and pianist"
],
"name": [
"Hershey Felder"
]
},
{
"relation": [
"spouse"
],
"details": [
"Canadian mathematician, chess master, chess writer, chess official (1925-2012)"
],
"name": [
"Nathan Divinsky"
]
}
],
"sanction_details": [],
"summary": {
"address": [],
"alias": [
"Kim Campbellová",
"Avril Phaedra Douglas Campbell",
"Кэмпбелл, Ким",
"Avril Phædra Douglas Campbell",
"کیم کمپبل",
"კიმ კემპბელი",
"キム・キャンベル",
"金·坎贝尔",
"Кім Кэмпбел",
"킴 캠벨",
"Ким Кембъл",
"Κιμ Κάμπελ",
"کم کیمبل",
"किम क्याम्पबेल",
"كيم كامبل",
"Kim Campbell",
"קים קמפבל",
"Քիմ Քեմփբել",
"Ким Кэмпбелл",
"Кім Кемпбелл",
"Ким Кэмбелл",
"Ким Кембел",
"Kim Kempbel"
],
"category": [
"PEP"
],
"country": [
"Canada"
],
"date_of_birth": [
"1947-03-10"
],
"date_of_death": [],
"description": [],
"designation": [],
"education": [
"Prince of Wales Secondary School",
"Peter A. Allard School of Law",
"University of British Columbia",
"The Royal Conservatory of Music",
"London School of Economics and Political Science"
],
"email": [],
"entity_type": [
"Person"
],
"first_name": [
"Avril"
],
"gender": [
"female"
],
"keywords": [
"National government"
],
"last_name": [
"Campbell"
],
"name": [
"Kim Campbell"
],
"nationality": [
"Canada"
],
"net_worth": [],
"notes": [
"19th Prime Minister of Canada in 1993"
],
"occupation": [
"Politician"
],
"phone": [],
"place_of_birth": [
"Port Alberni Canada",
"Port Alberni"
],
"place_of_death": [],
"political_party": [],
"position": [
"Prime Minister of Canada (1993-1993)",
"member of the House of Commons of Canada"
],
"position_occupancies": [
"member of the House of Commons of Canada"
],
"religion": [
"Anglican Church of Canada"
],
"suffix": [],
"title": [],
"website": [
"http://www.kimcampbell.com/"
]
}
},
"description": "Wikidata serves as the source of much of the data related to politically exposed persons (PEPs). The Wikidata importer will also traverse family and personal relationships that are documented in the database and import relatives and close associates whereever these are stated.",
"publisher": "Wikidata",
"url": "https://www.wikidata.org/wiki/Wikidata:Main_Page"
}
]
},
],
"search_reference": "643211e2daaeadac04832eb5",
"searched_name": "Kim Campbell",
"total_records": 20
},
"error": false,
"status": "SUCCESS"
}
Sample Response
{
"data": {
"adverse_media_status": "PENDING",
"client_reference": "",
"match_status": "Potential Match",
"case_status": "Passed",
"pagination": {
"current_page": 1,
"records_per_page": 160,
"total_pages": 1,
"total_records": 20
},
"results": [
{
"birth_incorporation_date": [
"1947-03-10"
],
"categories": [
"SIP",
"PEP"
],
"countries": [
"International",
"Canada"
],
"data": {
"additional_information": {
"twitter_handle": [
"akimcampbell"
]
},
"identification_documents": [],
"legal_notice": [
"The information contained in this report is derived from public sources such as Official Government Websites, Global Watchlists, Compliance Reports, Published Research Articles and News Sources. AML Watcher® is not the source of the data and is not responsible for the content of third party sources. AML Watcher® does not determine any positive or negative risks associated with the profiled entity. These decisions are solely determined by our clients as mandated by their applicable regulatory obligations."
],
"linked_entities": [
{
"relation": [
"spouse"
],
"details": [
"Canadian actor, playwright, and pianist"
],
"name": [
"Hershey Felder"
]
},
{
"relation": [
"spouse"
],
"details": [
"Canadian mathematician, chess master, chess writer, chess official (1925-2012)"
],
"name": [
"Nathan Divinsky"
]
}
],
"sanction_details": [],
"summary": {
"address": [],
"alias": [
"Kim Campbellová",
"Avril Phaedra Douglas Campbell",
"Кэмпбелл, Ким",
"Avril Phædra Douglas Campbell",
"کیم کمپبل",
"კიმ კემპბელი",
"キム・キャンベル",
"金·坎贝尔",
"Кім Кэмпбел",
"킴 캠벨",
"Ким Кембъл",
"Κιμ Κάμπελ",
"کم کیمبل",
"किम क्याम्पबेल",
"كيم كامبل",
"Kim Campbell",
"קים קמפבל",
"Քիմ Քեմփբել",
"Ким Кэмпбелл",
"Кім Кемпбелл",
"Ким Кэмбелл",
"Ким Кембел",
"Kim Kempbel"
],
"category": [
"PEP"
],
"country": [
"Canada"
],
"date_of_birth": [
"1947-03-10"
],
"date_of_death": [],
"description": [],
"designation": [],
"education": [
"Prince of Wales Secondary School",
"Peter A. Allard School of Law",
"University of British Columbia",
"The Royal Conservatory of Music",
"London School of Economics and Political Science"
],
"email": [],
"entity_type": [
"Person"
],
"first_name": [
"Avril"
],
"gender": [
"female"
],
"keywords": [
"National government"
],
"last_name": [
"Campbell"
],
"name": [
"Kim Campbell"
],
"nationality": [
"Canada"
],
"net_worth": [],
"notes": [
"19th Prime Minister of Canada in 1993"
],
"occupation": [
"Politician"
],
"phone": [],
"place_of_birth": [
"Port Alberni Canada",
"Port Alberni"
],
"place_of_death": [],
"political_party": [],
"position": [
"Prime Minister of Canada (1993-1993)",
"member of the House of Commons of Canada"
],
"position_occupancies": [
"member of the House of Commons of Canada"
],
"religion": [
"Anglican Church of Canada"
],
"suffix": [],
"title": [],
"website": [
"http://www.kimcampbell.com/"
]
}
},
"entity_types": [
"Entity",
"Person"
],
"id": "TZVsTJNDuSkerVtLPzxyDK",
"matched_alias": "",
"matched_names": [
{
"matched_name": "Kim Campbell",
"matching_fields": {},
"record_id": "TZVsTJNDuSkerVtLPzxyDK",
"score": "100",
"source_ids": [
"431699"
]
},
{
"matched_name": "Kim Campbell",
"matching_fields": {
"alias": false,
"birth_incorporation_date": true,
"image": false,
"profile_name": true
},
"record_id": "L8ayNgJBRH4FtzLfGstxLN",
"score": "100",
"source_ids": [
"17109391",
"431871"
]
}
],
"matched_rca": "",
"name": "Kim Campbell",
"relevance_status": {
"alias": false,
"birth_incorporation_date": false,
"category": true,
"country": false,
"entity_type": false,
"image_match": false,
"potential_match": false,
"profile_name": true,
"rca_name": false,
"unique_identifier": false
},
"risk_audit": {
"category_rules": {
"fitness and probity": {
"absolute_score": 80,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"insolvency": {
"absolute_score": 80,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"pep": {
"absolute_score": 70,
"weightage": 50,
"weighted_score": 35.0
},
"pep level 1": {
"absolute_score": 70,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"pep level 2": {
"absolute_score": 49,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"pep level 3": {
"absolute_score": 40,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"pep level 4": {
"absolute_score": 25,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"rca": {
"absolute_score": 49,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"sanctions": {
"absolute_score": 100,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"sie": {
"absolute_score": 90,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"sip": {
"absolute_score": 90,
"selected": true,
"weightage": 50,
"weighted_score": 45.0
},
"warnings and regulatory enforcement": {
"absolute_score": 90,
"selected": false,
"weightage": 50,
"weighted_score": 0
}
},
"country_rules": {
"afghanistan": {
"absolute_score": 75.71,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
"albania": {
"absolute_score": 38.63,
"selected": false,
"weightage": 50,
"weighted_score": 0
},
....
}
},
"risk_decision": "Failed",
"risk_level": "High",
"risk_score": 95,
"risk_score_engine_id": "65ce1f26a74c6232ed9ce828",
"risk_title": "AML Default Risk",
"risk_view": {
"categories": {
"risk_level": "Medium",
"risk_scores": {
"PEP": 70,
"SIP": 90
},
"score": 45.0,
"weightage": 50
},
"countries": {
"risk_level": "Medium",
"risk_scores": {
"Canada": 25.41,
"International": 100
},
"score": 50.0,
"weightage": 50
},
"crimes": {
"risk_level": "Low",
"risk_scores": {},
"score": 0,
"weightage": 0
},
"custom_list": {
"risk_level": "Low",
"risk_scores": {},
"score": 0,
"weightage": 0
}
},
"source_details": [
{
"categories": [
"PEP",
"SIP"
],
"countries": [
"International"
],
"description": "Pantheon is project that uses biographical data to expose patterns of human collective memory. Pantheon contains data on more than 70k biographies, which Pantheon distributes through a powerful data visualization engine centered on locations, occupations, and biographies. Pantheon’s biographical data contains information on the age, occupation, place of birth, and place of death, of historical characters with a presence in more than 15 language editions of Wikipedia. Pantheon also uses real-time data from the Wikipedia API to show the dynamics of attention received by historical characters in different Wikipedia language editions.",
"publisher": "Pantheon World",
"url": "https://pantheon.world/"
},
{
"categories": [
"PEP"
],
"countries": [
"Canada"
],
"description": "Wikidata serves as the source of much of the data related to politically exposed persons (PEPs). The Wikidata importer will also traverse family and personal relationships that are documented in the database and import relatives and close associates whereever these are stated.",
"publisher": "Wikidata",
"url": "https://www.wikidata.org/wiki/Wikidata:Main_Page"
}
]
},
],
"search_reference": "643211e2daaeadac04832eb5",
"searched_name": "Kim Campbell",
"total_records": 20
},
"error": false,
"status": "SUCCESS"
}
Response
| Parameter | Description |
|---|---|
| error | Indicates whether an error occurred during the API request. In this response, "error": false indicates that the request was successful without errors. |
| status | The status of the API request. |
| data | The main payload containing detailed information retrieved from the API. |
| data.adverse_media_status | The status of the adverse media check. |
| data.client_reference | The client reference ID for search reference. |
| data.match_status | The status of the match. "Potential Match" indicates that the searched case is a match. |
| data.case_status | Represents the overall outcome of an AML search case, reflecting the majority status of records as Passed, Failed, or For Review, determined by the results of the associated AML case. |
| data.pagination | Object containing pagination details. |
| data.pagination.current_page | The current page number of the results. |
| data.pagination.records_per_page | The number of records per page. |
| data.pagination.total_pages | The total number of pages. |
| data.pagination.total_records | The total number of records. |
| data.results | An array of result objects, each containing detailed information about the case. |
| data.results[].birth_incorporation_date | A string value containing the birth or incorporation date. |
| data.results[].categories | An array of categories associated with the entity. |
| data.results[].countries | An array of countries associated with the entity. |
| data.results[].data | An object containing additional data about the entity. |
| data.results[].data.additional_information | Object containing additional information such as social media handles. |
| data.results[].data.identification_documents | An array containing identification documents. |
| data.results[].data.legal_notice | An array containing legal notices related to the data. |
| data.results[].data.linked_entities | An array of linked entities with descriptions and details. |
| data.results[].data.sanction_details | An array containing details of any sanctions, which is empty in this response. |
| data.results[].data.summary | Object containing summary information about the entity. |
| data.results[].entity_types | An array containing the types of entities. |
| data.results[].id | The unique identifier for the entity. |
| data.results[].matched_alias | The matched alias. |
| data.results[].matched_names | An array containing matched names and their details. |
| data.results[].matched_rca | An array of matched relatives and close associates (RCA). |
| data.results[].name | The name of the entity. |
| data.results[].relevance_status | Object containing the relevance status for various fields. |
| data.results[].risk_audit | Object containing the risk audit details. |
| data.results[].risk_audit.category_rules | This object contains category-specific rules and their associated scores as defined in a custom or default risk scoring engine. For instance, if a category is listed with a predefined weightage, the system checks for its presence in the response data. If found, it calculates the weighted score and sets selected key to true according to the configurations established during the setup of the custom or default risk scoring mechanism. |
| data.results[].risk_audit.country_rules | This object contains country-specific rules and their associated scores as defined in a custom or default risk scoring engine. For instance, if a country is listed with a predefined weightage, the system checks for its presence in the response data. If found, it calculates the weighted score and sets selected key to true according to the configurations established during the setup of the custom or default risk scoring mechanism. |
| data.results[].risk_decision | The risk decision for the entity. |
| data.results[].risk_level | The risk level for the entity. |
| data.results[].risk_score | The risk score for the entity. |
| data.results[].risk_score_engine_id | The ID of the risk score engine used. |
| data.results[].risk_title | The title of the risk. |
| data.results[].risk_view | Object containing the risk view details. |
| data.results[].risk_view.categories | Object containing risk view details by category. |
| data.results[].risk_view.countries | Object containing risk view details by country. |
| data.results[].risk_view.crimes | Object containing risk view details by crimes. |
| data.results[].risk_view.custom_list | Object containing risk view details by custom list. |
| data.results[].source_details | An array containing details of the sources used for the data. |