US 2023/0410238 A1Published application

Assisting at Risk Individuals

AssigneeBank of America Corporation

InventorStacy Bennett

Life of this patent

Estimating the term…

Loading patent…

Abstract

Computer-implemented human trafficking detection utilizing artificial intelligence and/or machine learning—is disclosed to detect illicit businesses through sex-buyer behavior and the merchants they frequent. Customers exhibiting sex buyer patterns of behavior are identified and scored based on multiple factors. Merchants paid by these flagged customers are scored based on the behavior of all their customers (not only their sex buyer flagged customers) to measure the extent of illicit and legitimate services provided, and identify those likely involved in human trafficking. Similarity function(s) match likely illicit merchants to customers' business accounts. Matching business accounts can be measured for any additional red flags. Once business accounts for IMBs are detected, entire criminal networks can be identified based on shared social data (addresses, phone numbers, TINs) of the IMB customer profile as well as identifying any counterparties doing business with the IMB accounts.

Claims20

Dependent claims

A computer-implemented human-trafficking detection process comprising the steps of:

(a) identifying, by a human-trafficking detection server from a subscriber datastore, a list of one or more subscribers to analyze based on detection criteria;
(b) retrieving, by the human-trafficking detection server from the subscriber datastore into a first sector of server memory, one or more subscribers to analyze;
(c) retrieval, by the human-trafficking detection server from a financial transaction datastore into a second sector of the server memory, a plurality of subscriber financial transactions that were executed by said one or more subscribers, said plurality of subscriber transactions having transaction amounts;
(d) storing, by the human-trafficking detection server in a third sector of server memory, the transaction amounts by transaction date;
(e) filtering, by the human-trafficking detection server based on business type, the plurality of subscriber financial transactions to identify suspect businesses potentially providing sex-buyer services;
(f) storing, by the human-trafficking detection server in a fourth sector of server memory, the plurality of subscriber financial transactions;
(g) retrieving, by the human-trafficking detection server from an ATM datastore, cash withdrawals and cash advances by the subscriber;
(h) storing, by the human-trafficking detection server in a fifth sector of server memory, the cash withdrawals and the cash advances by ATM date;
(i) calculating, by the human-trafficking detection server, summations of the cash withdrawals, the cash advances, and the transaction amounts based on the transaction date and the ATM date;
(j) storing, by the human-trafficking detection server into a sixth sector of the server memory, the summations based on the transaction date and the ATM date;
(k) identifying, by the human-trafficking detection server, potential sex businesses from the suspect businesses based on a correlation of the summations of the cash withdrawals and the cash advances to the transaction amounts by said transaction date and said ATM date, wherein said suspect businesses are more likely to be said potential sex businesses based on a concentration of a quantity of subscribers having higher average summations;
and
(l) storing, by the human-trafficking detection server into a seventh sector of server memory, the potential sex businesses.

10 more elements

A computer-implemented artificial-intelligence based human-trafficking detection process comprising the steps of:

(a) identifying, by a human-trafficking detection server from a subscriber datastore, a list of one or more subscribers to analyze based on detection criteria;
(b) retrieving, by the human-trafficking detection server from the subscriber datastore into a first sector of server memory, one or more subscribers to analyze;
(c) retrieval, by the human-trafficking detection server from a financial transaction datastore into a second sector of the server memory, a plurality of subscriber financial transactions that were executed by said one or more subscribers, said plurality of subscriber transactions having transaction amounts;
(d) storing, by the human-trafficking detection server in a third sector of server memory, the transaction amounts by transaction date;
(e) filtering, by the human-trafficking detection server based on merchant category code, the plurality of subscriber financial transactions to identify suspect businesses potentially providing sex-buyer services;
(f) storing, by the human-trafficking detection server in a fourth sector of server memory, the plurality of subscriber financial transactions;
(g) retrieving, by the human-trafficking detection server from an ATM datastore, cash withdrawals and cash advances by the subscriber;
(h) storing, by the human-trafficking detection server in a fifth sector of server memory, the cash withdrawals and the cash advances by ATM date;
(i) calculating, by the human-trafficking detection server, summations of the cash withdrawals, the cash advances, and the transaction amounts based on the transaction date and the ATM date;
(j) storing, by the human-trafficking detection server into a sixth sector of the server memory, the summations based on the transaction date and the ATM date;
(k) utilizing, by the human-trafficking detection server, artificial intelligence to identify potential sex businesses from the suspect businesses based on:
(i) a correlation of the summations of the cash withdrawals and the cash advances to the transaction amounts by said transaction date and said ATM date, wherein said suspect businesses are more likely to be said potential sex businesses based on a concentration of a quantity of subscribers having higher average summations,
(ii) one or more IMB detection steps,
(iii)one or more IMB scoring metrics;
and
(l) storing, by the human-trafficking detection server into a seventh sector of server memory, the potential sex businesses.

13 more elements

A computer-implemented artificial-intelligence based human-trafficking detection process comprising the steps of:

(a) identifying, by a human-trafficking detection server from a subscriber datastore, a list of one or more subscribers to analyze based on detection criteria;
(b) retrieving, by the human-trafficking detection server from the subscriber datastore into a first sector of server memory, one or more subscribers to analyze;
(c) retrieving, by the human-trafficking detection server from a financial transaction datastore into a second sector of the server memory, a plurality of subscriber financial transactions that were executed by said one or more subscribers, said plurality of subscriber transactions having transaction amounts;
(d) storing, by the human-trafficking detection server in a third sector of server memory, the transaction amounts by transaction date;
(e) filtering, by the human-trafficking detection server based on merchant category code, the plurality of subscriber financial transactions to identify suspect businesses potentially providing sex-buyer services;
(f) storing, by the human-trafficking detection server in a fourth sector of server memory, the plurality of subscriber financial transactions;
(g) retrieving, by the human-trafficking detection server from an ATM datastore, cash withdrawals and cash advances by the subscriber;
(h) storing, by the human-trafficking detection server in a fifth sector of server memory, the cash withdrawals and the cash advances by ATM date;
(i) calculating, by the human-trafficking detection server, summations of the cash withdrawals, the cash advances, and the transaction amounts based on the transaction date and the ATM date;
(j) storing, by the human-trafficking detection server into a sixth sector of the server memory, the summations based on the transaction date and the ATM date;
(k) utilizing, by the human-trafficking detection server, artificial intelligence to identify potential sex businesses from the suspect businesses based on:
(i) a correlation of the summations of the cash withdrawals and the cash advances to the transaction amounts by said transaction date and said ATM date, wherein said suspect businesses are more likely to be said potential sex businesses based on a concentration of a quantity of subscribers having higher average summations,
(ii) one or more IMB detection steps selected from the group consisting of:
(1) determining, by the human-trafficking detection server, a sex buyer site subscriber percent,
(2) calculating, by the human-trafficking detection server, an average spa/salon payment transaction amount,
(3) identification, by the human-trafficking detection server, a same day withdrawal percentage,
(4) calculating, by the human-trafficking detection server, an average same day withdrawal amount,
(5) determining, by the human-trafficking detection server, a network party count,
(6) determining, by the human-trafficking detection server, a network spa count,
(7) calculating, by the human-trafficking detection server, a cash deposit incoming transaction percentage,
(8) calculating, by the human-trafficking detection server, a cash withdrawal outgoing transaction percentage,
(9) identifying, by the human-trafficking detection server, a commercial sex advertisement transaction count,
(10) determining, by the human-trafficking detection server, whether one or more said potential sex businesses has a suspicious activity report indicator,
(11) determining, by the human-trafficking detection server, whether one or more said potential sex businesses has a NAICS code or business description related to logistics, freight, or transport,
(12) calculating, by the human-trafficking detection server, a cryptocurrency transaction account,
(13) calculating, by the human-trafficking detection server, a burner phone transaction count;
and
(iii) one or more IMB scoring metric steps selected from the group consisting of:
(1) determining, by the human-trafficking detection server, a sex buyer site subscriber percent,
(2) calculating, by the human-trafficking detection server, an average spa/salon payment transaction amount,
(3) identification, by the human-trafficking detection server, a same day withdrawal percentage,
(4) calculating, by the human-trafficking detection server, an average same day withdrawal amount,
(5) determining, by the human-trafficking detection server, a network party count;
(6) determining, by the human-trafficking detection server, a network spa count;
(7) calculating, by the human-trafficking detection server, a cash deposit incoming transaction percentage,
(8) calculating, by the human-trafficking detection server, a cash withdrawal outgoing transaction percentage,
(9) identifying, by the human-trafficking detection server, a commercial sex advertisement transaction count,
(10) determining, by the human-trafficking detection server, whether one or more said potential sex businesses has a suspicious activity report indicator,
(11) determining, by the human-trafficking detection server, whether one or more said potential sex businesses has a NAICS code or business description related to logistics, freight, or transport,
(12) calculating, by the human-trafficking detection server, a cryptocurrency transaction account,
(13) calculating, by the human-trafficking detection server, a burner phone transaction count;
and
(l) storing, by the human-trafficking detection server into a seventh sector of server memory, the potential sex businesses.

40 more elements

Dependencies are inferred from the claim text.