Automobile Monitoring Systems and Methods for Loss Reserving and Financial Reporting

Abstract

A method of determining loss reserves and/or providing automatic financial reporting related thereto via one or more processors includes (1) receiving a plurality of historical electronic claim documents, each respectively labeled with a claim loss amount; (2) normalizing each respective claim loss amount and training an artificial intelligence or machine learning algorithm, module, or model, such as an artificial neural network, by applying the plurality of electronic claim documents to the artificial intelligence or machine learning algorithm, module, or model. The method may include receiving a user claim and predicting a loss reserve amount by applying the user claim to the trained artificial intelligence or machine learning algorithm, module, or model, and may include unreported claims.

1 - 20 . (canceled) 
     
     
         21 . A method implemented by one or more processors, comprising:
 receiving a plurality of historical claim documents;   rendering a graphical user interface to a user, the graphical user interface comprising a selection section and a query section;   receiving a user input corresponding to the selection section;   receiving a user query corresponding to the query section;   selecting a subset of historical claim documents from the plurality of historical claims based at least in part upon the user input and the user query;   training a machine learning model using the selected subset of historical claim documents;   receiving a claim document; and   predicting a loss reserve amount by applying the trained machine learning model to the claim document;   
     
     
         22 . The method of  claim 21 , further comprising:
 selecting a first subset of historical claim documents based upon the user input;   selecting a second subset of historical claim documents based upon the user query; and   generating the subset of historical claim documents based upon the first subset of historical claim documents and the second subset of historical claim documents.   
     
     
         23 . The method of  claim 21 , further comprising:
 compiling a structured query based upon the user query; and   selecting the subset of historical claim documents from the plurality of historical claims based at least in part upon the user input and the structured query.   
     
     
         24 . The method of  claim 21 , wherein the machine learning model comprises a first machine learning model and a second machine learning model, the method further comprising:
 selecting a first subset of historical claim documents based upon the user input;   selecting a second subset of historical claim documents based upon the user query;   training the first machine learning model using the first subset of historical claim documents;   training the second machine learning model using the second subset of historical claim document;   predicting a first loss reserve amount by applying the first trained machine learning model to the claim document;   predicting a second loss reserve amount by applying the second trained machine learning model to the claim document; and   determining the loss reserve amount based at least in part upon the first loss reserve amount and the second loss reserve amount.   
     
     
         25 . The method of  claim 24 , further comprising:
 determining the loss reserve amount to be the sum of the first loss reserve amount and the second loss reserve amount.   
     
     
         26 . The method of  claim 21 , wherein the claim document includes free-form text and an image, the method further comprising:
 determining a first cause of loss by applying the trained machine learning model to the free-form text of the claim document;   determining a second cause of loss by applying the trained machine learning model to the image of the claim document; and   predicting the loss reserve amount using the trained machine learning model based at least in part upon the first cause of loss and the second cause of loss.   
     
     
         27 . The method of  claim 26 , wherein the trained machine learning model comprising a natural language processing model, the method further comprising:
 identifying a keyword in the free-form text of the claim document using the natural language processing model; and   determining the first cause of loss based at least upon the identified keyword.   
     
     
         28 . The method of  claim 21 , wherein each historical claim document of the plurality of history claim documents is associated with a plurality of labels. 
     
     
         29 . A system, comprising:
 one or more memories having instructions stored thereon; and   one or more processors configured to execute the instructions and configured to perform operations comprising:
 receiving a plurality of historical claim documents; 
 rendering a graphical user interface to a user, the graphical user interface comprising a selection section and a query section; 
 receiving a user input corresponding to the selection section; 
 receiving a user query corresponding to the query section; 
 selecting a subset of historical claim documents from the plurality of historical claims based at least in part upon the user input and the user query; 
 training a machine learning model using the selected subset of historical claim documents; 
 receiving a claim document; and 
 predicting a loss reserve amount by applying the trained machine learning model to the claim document; 
   
     
     
         30 . The system of  claim 29 , wherein the operations further comprise:
 selecting a first subset of historical claim documents based upon the user input;   selecting a second subset of historical claim documents based upon the user query; and   generating the subset of historical claim documents based upon the first subset of historical claim documents and the second subset of historical claim documents.   
     
     
         31 . The system of  claim 29 , wherein the operations further comprise:
 compiling a structured query based upon the user query; and   selecting the subset of historical claim documents from the plurality of historical claims based at least in part upon the user input and the structured query.   
     
     
         32 . The system of  claim 29 , wherein the machine learning model comprises a first machine learning model and a second machine learning model, wherein the operations further comprise:
 selecting a first subset of historical claim documents based upon the user input;   selecting a second subset of historical claim documents based upon the user query;   training the first machine learning model using the first subset of historical claim documents;   training the second machine learning model using the second subset of historical claim document;   predicting a first loss reserve amount by applying the first trained machine learning model to the claim document;   predicting a second loss reserve amount by applying the second trained machine learning model to the claim document; and   determining the loss reserve amount based at least in part upon the first loss reserve amount and the second loss reserve amount.   
     
     
         33 . The system of  claim 32 , wherein the operations further comprise:
 determining the loss reserve amount to be the sum of the first loss reserve amount and the second loss reserve amount.   
     
     
         34 . The system of  claim 29 , wherein the claim document includes free-form text and an image, wherein the operations further comprise:
 determining a first cause of loss by applying the trained machine learning model to the free-form text of the claim document;   determining a second cause of loss by applying the trained machine learning model to the image of the claim document; and   predicting the loss reserve amount using the trained machine learning model based at least in part upon the first cause of loss and the second cause of loss.   
     
     
         35 . The system of  claim 34 , wherein the trained machine learning model comprising a natural language processing model, wherein the operations further comprise:
 identifying a keyword in the free-form text of the claim document using the natural language processing model; and   determining the first cause of loss based at least upon the identified keyword.   
     
     
         36 . The system of  claim 29 , wherein each historical claim document of the plurality of history claim documents is associated with a plurality of labels. 
     
     
         37 . A non-transitory computer-readable storage media comprising instructions that cause a programmable processor to:
 receive a plurality of historical claim documents;   render a graphical user interface to a user, the graphical user interface comprising a selection section and a query section;   receive a user input corresponding to the selection section;   receive a user query corresponding to the query section;   select a subset of historical claim documents from the plurality of historical claims based at least in part upon the user input and the user query;   train a machine learning model using the selected subset of historical claim documents;   receive a claim document; and   predict a loss reserve amount by applying the trained machine learning model to the claim document.   
     
     
         38 . The non-transitory computer-readable storage media of  claim 37 , wherein the instructions further cause the programmable processor to:
 select a first subset of historical claim documents based upon the user input;   select a second subset of historical claim documents based upon the user query; and   generate the subset of historical claim documents based upon the first subset of historical claim documents and the second subset of historical claim documents.   
     
     
         39 . The non-transitory computer-readable storage media of  claim 37 , wherein the instructions further cause the programmable processor to:
 compile a structured query based upon the user query; and   select the subset of historical claim documents from the plurality of historical claims based at least in part upon the user input and the structured query.   
     
     
         40 . The non-transitory computer-readable storage media of  claim 37 , wherein the instructions further cause the programmable processor to:
 determine a first cause of loss by applying the trained machine learning model to the free-form text of the claim document;   determine a second cause of loss by applying the trained machine learning model to the image of the claim document; and   predict the loss reserve amount using the trained machine learning model based at least in part upon the first cause of loss and the second cause of loss.