System and method for ai-based loan processing

A method comprising:

• acquiring, over a network, user data from an entity, the user data comprising electronic documents for the user;
• converting the user data from a first format to second format, the second format enabling analysis of the electronic documents as compared to other user data for other users;
• analyzing the user data in the second format by performing a computational analysis on the user data, and determining, based on the computational analysis, a plurality of features;
• searching, over the network, a local users database based on a query comprising the plurality of features, the search causing electronic retrieval of historical users related data, the retrieved local historical users related data comprising electronic documents for local historical users;
• generating at least one feature vector based on the plurality of features and the local historical users-related data;
• executing a machine learning (ML) model, the execution comprising providing the at least one feature vector as input to the ML model;
• generating, via execution of the ML model, a predictive model, the predictive model being a newly created ML model for controlling how digital assets are transferred to electronic accounts of users, the generation comprising the predictive model being stored in storage for subsequent usage and training;
• executing the predictive model, and producing, based on the execution of the predictive model, at least one lending parameter, the at least one lending parameter being a dynamically adjustable parameter that controls how the digital assets are to be transferred to the electronic accounts of the users;
• outputting, based on the execution of the generated predictive model, a user-related lending verdict, the user-related lending verdict comprising functionality for structuring digital asset assignment and availability;
• recording the user-related lending verdict in the storage;
• training the predictive model based on information in the storage;
• responsive to a request for transfer of digital assets to another user, retrieving user data for the other user and other local historical users' data;
• executing the trained predictive model to analyze the retrieved user data for the other user and the other local historical users' data:
• determining, based on the execution of the trained predictive model, a recommendation for responding to the request comprising a lending verdict for the other user;
• establishing, over the network, a secure communication chat channel, the chat channel comprising a large language model (LLM) chat bot;
• communicate, via the secure communication chat channel, with the user via the chat bot, the communication comprising an exchange of information related to the recommendation;
• modifying, based on the communication and via the chat bot, the recommendation;
• and
• controlling, over the network, the transfer of the digital assets based on the modified recommendation.

A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor, performs a method comprising:

• acquiring, over a network, user data from an entity, the user data comprising electronic documents for the user;
• converting the user data from a first format to second format, the second format enabling analysis of the electronic documents as compared to other user data for other users;
• analyzing the user data in the second format by performing a computational analysis on the user data, and determining, based on the computational analysis, a plurality of features;
• searching, over the network, a local users database based on a query comprising the plurality of features, the search causing electronic retrieval of historical users related data, the retrieved local historical users related data comprising electronic documents for local historical users;
• generating at least one feature vector based on the plurality of features and the local historical users-related data;
• executing a machine learning (ML) model, the execution comprising providing the at least one feature vector as input to the ML model;
• generating, via execution of the ML model, a predictive model, the predictive model being a newly created ML model for controlling how digital assets are transferred to electronic accounts of users, the generation comprising the predictive model being stored in storage for subsequent usage and training;
• executing the predictive model, and producing, based on the execution of the predictive model, at least one lending parameter, the at least one lending parameter being a dynamically adjustable parameter that controls how the digital assets are to be transferred to the electronic accounts of the users;
• outputting, based on the execution of the generated predictive model, a user-related lending verdict, the user-related lending verdict comprising functionality for structuring digital asset assignment and availability;
• recording the user-related lending verdict in the storage;
• training the predictive model based on information in the storage;
• responsive to a request for transfer of digital assets to another user, retrieving user data for the other user and other local historical users' data;
• executing the trained predictive model to analyze the retrieved user data for the other user and the other local historical users' data;
• determining, based on the execution of the trained predictive model, a recommendation for responding to the request comprising a lending verdict for the other user;
• establishing, over the network, a secure communication chat channel, the chat channel comprising a large language model (LLM) chat bot;
• communicating, via the secure communication chat channel, with the user via the chat bot, the communication comprising an exchange of information related to the recommendation;
• modifying, based on the communication and via the chat bot, the recommendation;
• and
• controlling, over the network, the transfer of the digital assets based on the determined recommendation.