Systems and methods for obtaining incident information to reduce fraud

A system for analyzing device-submitted documentation, the system comprising:

• a memory storing a set of computing instructions including a trained machine learning model;
• and
• a processor coupled to the memory, and configured to:
• receive one or more initial images related to a damaged property;
• analyze, using the trained machine learning model, the one or more initial images to generate a first result that comprises a determined amount of damage to the damaged property;
• determine an initial confidence level in the determined amount of damage based upon the first result;
• determine, using the trained machine learning model, that additional documentation is needed when the initial confidence level does not exceed an initial threshold level;
• in response to determining that the additional documentation is needed, transmit to a user, a set of instructions for capturing an additional set of one or more images further depicting the damaged property, wherein the set of instructions indicates a time limit for capturing the additional set of one or more images;
• receive the additional set of one or more images;
• analyze, using the trained machine learning model, the additional set of one or more images, to generate a second result that comprises a confidence level, wherein analyzing the additional set of one or more images also includes (i) determining whether the one or more images of the additional set correspond to the set of instructions and (ii) analyzing, using the trained machine learning model, metadata associated with the additional set of one or more images to generate a metadata analysis result;
• and (iii) determining whether the additional set of one or more images was captured and submitted within the time limit;
• determine, using the trained machine learning model, whether the confidence level exceeds a threshold level;
• and
• notify the user, of an outcome corresponding to whether the confidence level exceeds the threshold level.

A computer-implemented method of analyzing device-submitted documentation, the method comprising:

• receiving, by one or more processors, one or more initial images related to a damaged property;
• analyzing, by the one or more processors using a trained machine learning model, the one or more initial images to generate a first result that comprises a determined amount of damage to the damaged property;
• determining, by the one or more processors, an initial confidence level in the determined amount of damage based upon the first result;
• determining, by the one or more processors using the trained machine learning model, that additional documentation is needed when the initial confidence level does not exceed an initial threshold level;
• in response to determining that the additional documentation is needed, transmitting to a user, a set of instructions for capturing an additional set of one or more images further depicting the damaged property, wherein the set of instructions indicates a time limit for capturing the additional set of one or more images;
• receiving, by the one or more processors, the additional set of one or more images;
• analyzing, by the one or more processors using the trained machine learning model, the additional set of one or more images, to generate a second result that comprises a confidence level, wherein analyzing the additional set of one or more images also includes (i) determining whether the one or more images of the additional set correspond to the set of instructions and (ii) analyzing, using the trained machine learning model, metadata associated with the additional set of one or more images to generate a metadata analysis result;
• and (iii) determining whether the additional set of one or more images was captured and submitted within the time limit;
• determining, by the one or more processors using the trained machine learning model, whether the confidence level exceeds a threshold level;
• and
• notifying the user, by the one or more processors, of an outcome corresponding to whether the confidence level exceeds the threshold level.