Systems and methods for client intake and management using risk parameters

A method for client intake and management by a first party, the method comprising:

• presenting, on a user terminal, a user interface enabling a user to enter a name of a second party that is a potential client;
• receiving, via the user interface, user input of initial client data including the name of the second party;
• transmitting the initial client data from the user terminal to a central server including a memory storing an extraction algorithm including a neural network and a matter database;
• searching the name of the second party in the matter database using a hierarchal searching system to calculate a risk assessment for the second party based at least partially on past hierarchal searching results;
• determining a conflict rating code based on subsequent search results from the searching the name of the second party and the past hierarchal searching results;
• determining a litigiousness factor weighted as part of the risk assessment for the second party by matching the user input of initial client data against the matter database on the central server;
• in response to not finding the name of the second party in the matter database, retrieving a document or website including additional client data regarding the second party from an existing second party database or a third party data source;
• based on classification of the document or website, using the extraction algorithm including the neural network to extract target data from the additional client data based on x and y coordinates defining a location on the document or website;
• receiving input regarding whether the target data at the x and y coordinates is accurate;
• when the input indicates that the extracted target data is accurate, using the extracted target data to further train the extraction algorithm including the neural network as a positive example of a location of x and y coordinates where target information is found on the classified document or website;
• when the input indicates that the extracted target data is not accurate, using the extracted target data to further train the extraction algorithm including the neural network as a negative example of the location of x and y coordinates where target information is found on the classified document or website;
• generating at least one risk parameter based on the target data;
• determining a risk associated with the second party based on the at least one risk parameter;
• updating the conflict rating code;
• and
• presenting the risk to the user using the user interface of the user terminal.