Systems and methods for obtaining incident information to reduce fraud
Systems and methods for analyzing documentation for assessing potential fraudulent user submissions are provided. According to certain aspects, a server computer may receive an initial set of documentation descriptive of damage to a property asset, and may analyze the initial set of documentation to determine whether additional documentation is needed. The server computer may initiate a communication channel with a user device via which the additional documentation may be submitted, and the server computer may similarly analyze the additional documentation to determine a likelihood of fraud. The server computer may process the user submission accordingly.
1 . 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, by the one or more processors, 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.
2 . The computer-implemented method of claim 1 , further comprising:
outputting, by the one or more processors using the trained machine learning model, an indication of completeness of the one or more initial images; and
determining, by the one or more processors using the trained machine learning model, the confidence level based at least in part upon the indication of completeness.
3 . The computer-implemented method of claim 1 , wherein
the trained machine learning model is trained using a set of training data.
4 . The computer-implemented method of claim 1 , wherein an image capture sensor is configured to capture the additional set of one or more images in accordance with the set of instructions.
5 . The computer-implemented method of claim 1 , further comprising:
determining, by the one or more processors using the trained machine learning model, the confidence level based at least in part on the metadata analysis result generated by analyzing metadata associated with the additional set of one or more images.
6 . The computer-implemented method of claim 1 further comprising:
receiving textual information describing the damaged property.
7 . The computer-implemented method of claim 6 , wherein determining the initial confidence level includes determining the initial confidence level based upon a comparison of the first result and the textual information describing the damaged property.
8 . The computer-implemented method of claim 7 , wherein determining the initial confidence level includes determining the initial confidence level using the trained machine learning model.
9 . 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.
10 . The system of claim 9 , wherein the processor is further configured to:
output, using the trained machine learning model, an indication of completeness of the one or more initial images; and
determine, using the trained machine learning model, the confidence level based at least in part upon the indication of completeness.
11 . The system of claim 9 , wherein the processor is further configured to:
use a set of training data to train the trained machine learning model.
12 . The system of claim 9 , wherein an image capture sensor is configured to capture the additional set of one or more images in accordance with the set of instructions.
13 . The system of claim 9 , wherein the processor is further configured to:
determine, using the trained machine learning model, the confidence level based at least in part on the metadata analysis result generated by analyzing metadata associated with the additional set of one or more images.
14 . The system of claim 9 , wherein the processor is further configured to:
receive textual information describing the damaged property.
15 . The system of claim 14 , wherein the processor is further configured to determine the initial confidence level based upon a comparison of the first result and the textual information describing the damaged property.
16 . The system of claim 15 , wherein the processor is further configured to determine the initial confidence level using the trained machine learning model.
17 . 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.
18 . The computer-implemented method of claim 17 further comprising:
receiving textual information describing the damaged property.
19 . The computer-implemented method of claim 18 , wherein determining the initial confidence level includes determining the initial confidence level based upon a comparison of the first result and the textual information describing the damaged property.
20 . The computer-implemented method of claim 19 , wherein determining the initial confidence level includes determining the initial confidence level using the trained machine learning model.