AI-based computer record compliance management system and method
A system, and method perform compliance checking of computer records for MLS services. The compliance checking includes detecting multiple-listing-service (MLS) contract compliance with a MLS, gathering MLS-related data regarding an MLS subscriber from at least one database, training an AI engine with input data containing as features MLS service requirements that correspond to contractual obligations of the MLS subscriber under a MLS license, applying the MLS-related data to the AI engine, after the AI engine is trained, and checking for a non-compliance incident of the MLS-related data with the MLS service requirements, and in response to detection of a non-compliance incident, trigger a computer-based resolution process to resolve the non-compliance incident.
1 . A computer-implemented method of detecting multiple-listing-service (MLS) contract compliance for a MLS, the method comprising:
gathering MLS-related data regarding an MLS subscriber, wherein the MLS-related data includes data associated with a particular broker or a particular agent of the MLS subscriber, from at least one database;
training an AI engine to detect a non-compliance incident from input data that contains MLS service requirements as features of the input data that correspond to contractual obligations of the MLS subscriber under a MLS license, wherein the training includes associating specific changes in MLS-related data, including at least one of a change in active/inactive status of a broker or an agent, adherence to MLS internet data exchange (IDX) policies, MLS logo compliance, or a change in broker office ID of the broker or the agent, with corresponding non-compliance incidents during training of the AI engine;
after the AI engine is trained, applying the MLS-related data to the AI engine and checking for the non-compliance incident of the MLS-related data with respect to the MLS service requirements; and
in response to detection of the non-compliance incident, triggering a computer-based resolution process to resolve the non-compliance incident, wherein the triggering includes executing an auto-locking process that blocks access to a web site that hosts live data of the MLS by a particular broker or a particular agent that is associated with the non-compliance incident of the MLS subscriber under the MLS license.
2 . The method of claim 1 , wherein:
the gathering includes gathering the MLS-related data from a plurality of disparate sources, at least one of the plurality of disparate sources being a roster of brokers and realtors.
3 . The method of claim 2 , wherein:
the plurality of disparate sources also includes an access log that includes entries of previous accesses to the MLS.
4 . The method of claim 3 , wherein:
the checking includes checking the access log with the AI engine to detect a pattern of accesses to the MLS by the particular broker or the particular agent.
5 . The method of claim 4 , wherein:
the checking includes detecting with the AI engine the change in active/inactive status or office affiliation for the particular broker or the particular agent.
6 . The method of claim 2 , wherein:
the plurality of sources also includes a source of at least a portion of the MLS IDX policies.
7 . The method of claim 2 , wherein:
the gathering includes repeatedly gathering the roster of brokers and realtors so as to capture updates in the roster of brokers and realtors, and
the checking includes repeatedly checking an active/inactive status for the particular broker or the particular agent so as to trigger the non-compliance incident in response to a change in the active/inactive status.
8 . The method of claim 2 , wherein:
the gathering includes repeatedly gathering the roster of brokers and realtors so as to capture updates in the roster of brokers and realtors, and
the checking includes repeatedly checking a broker office ID for the particular broker or a particular agent so as to trigger the incident in response to the change in the broker office ID.
9 . The method of claim 1 , further comprising:
in response to detection of the non-compliance incident, capturing in memory at least one of a reason that caused the non-compliance incident, a start date of the non-compliance incident, and a change of data associated with data that caused the non-compliance incident.
10 . The method of claim 1 , further comprising:
executing the computer-based resolution process to resolve the non-compliance incident.
11 . The method of claim 10 , wherein:
the computer-based resolution process to resolve the non-compliance incident includes causing a display of user-selectable prompts, and responding to a selection of the user-selectable prompts by executing a corresponding process.
12 . The method of claim 1 , wherein:
the gathering includes dispatching a software-bot to scrape the MLS-related data from a website of the MLS subscriber.
13 . The method of claim 12 , wherein
the MLS-related data includes a logo, and the AI engine is trained to detect a deviation of the logo with respect to the MLS service requirements.
14 . The method of claim 1 , wherein the training includes training the AI engine to detect the non-compliance incident as an anticipatory incident expected to occur based on the input data associated with the MLS-related data.
15 . A computer based system that detects multiple-listing-service (MLS) contract compliance for a MLS, the method comprising:
a memory that includes computer-readable instructions stored therein; and
processing circuitry configured to execute the computer-readable instructions so as to configure the processing circuitry to
gather MLS-related data regarding an MLS subscriber, wherein the MLS-related data includes data associated with a particular broker or a particular agent of the MLS subscriber, from at least one database,
train an AI engine to detect a non-compliance incident from input data that contains MLS service requirements as features of the input data that correspond to contractual obligations of the MLS subscriber under a MLS license, wherein the AI is trained to include associations of specific changes in MLS-related data, including at least one of a change in active/inactive status of a broker or an agent, adherence to MLS internet data exchange (IDX) policies, MLS logo compliance, or a change in broker office ID of the broker or the agent, with corresponding non-compliance incidents during training of the AI engine,
after the AI engine is trained, apply the MLS-related data to the AI engine and check for the non-compliance incident of the MLS-related data with respect to the MLS service requirements, and
in response to detection of the non-compliance incident, trigger a computer-based resolution process to resolve the non-compliance incident that auto-locks and blocks access to a web site that hosts live data of the MLS by the particular broker or the particular agent that is associated with the non-compliance incident of the MLS subscriber under the MLS license.
16 . The computer based system of claim 15 , wherein:
the processing circuitry is configured to gather the MLS-related data from a plurality of disparate sources, at least one of the plurality of disparate sources being a roster of brokers and realtors.
17 . The computer based system of claim 16 , wherein:
the plurality of sources also includes an access log that includes entries of previous accesses to the MLS.
18 . The computer based system of claim 17 , wherein:
the processing circuitry is configured to check the access log with the AI engine to detect a pattern of accesses to the MLS by the particular broker or the particular agent.
19 . A non-transitory computer readable storage device that has computer readable instructions stored therein that upon execution by a computer, cause the computer to implement a method of detecting multiple-listing-service (MLS) contract compliance with a MLS, the method comprising:
gathering MLS-related data regarding an MLS subscriber, wherein the MLS-related data includes data associated with a particular broker or a particular agent of the MLS subscriber, from at least one database;
training an AI engine to detect a non-compliance incident from input data that contains MLS service requirements as features of the input data that correspond to contractual obligations of the MLS subscriber under a MLS license, wherein the training includes associating specific changes in MLS-related data, including at least one of a change in active/inactive status of a broker or an agent, adherence to MLS internet data exchange (IDX) policies, MLS logo compliance, or a change in broker office ID of the broker or the agent, with corresponding non-compliance incidents during training of the AI engine;
after the AI engine is trained, applying the MLS-related data to the AI engine and checking for the non-compliance incident of the MLS-related data with respect to the MLS service requirements; and
in response to detection of the non-compliance incident, triggering a computer-based resolution process to resolve the non-compliance incident, wherein the triggering
includes executing an auto-locking process that blocks access to a web site that hosts live data of the MLS by the particular broker or the particular agent that is associated with the non-compliance incident of the MLS subscriber under the MLS license.