In one or more arrangements, a system is presented for automated processing and/or review of documents for denied benefit system claims and generation of letters to petition for reconsideration. In one or more arrangements, the system includes a front end system and a back end system that is communicatively connected to the front end system. The back end system is configured to perform one or more processes to automatically review documents for a denied benefit system claim and automatically generate a letter to petition for reconsideration. The front end system is configured to provide a user interface for a user to submit a denial letter for the denied benefit system claim and prompt back end system to perform the one or more processes to generate the letter to petition for reconsideration.

Machine learning systems and methods for property estimate anomaly detection are provided. The system receives property estimation data from a data source and processes the property estimation data to extract line item information from the property estimation data. The line item information, along with majority estimate information, is then processed by an automated anomaly detection process which performs majority estimate unit detection, line item quantity detection, and line item cluster detection on the extracted line item information using a plurality of machine learning models. The system then processes the majority estimate unit detection, line item quantity detection, and line item cluster detection to identify anomalous data in the line item information, and generates and displays a summary of the anomalous data in a graphical user interface screen of a claims estimation software application.

An information processing apparatus includes an acquisition unit for acquiring event information indicating a target event which is an event likely to be associated with a payment cause of insurance, and an extraction unit for extracting a related payment cause related to the target event from a document describing a payment cause of insurance using an extraction model trained by machine learning to output a portion related to the data in the document using a set of the document and the data as an input. The information processing apparatus supports decision making in insurance payment assessment.

Generative artificial intelligence systems and methods for processing insurance underwriting data are provided. The system automatically ingests disparate underwriting data of varying degrees of complexity, deconstructs such files and maps them to a standardized object format, assesses the accuracy and completeness of the mapping, and automatically performs repetitive underwriting data processing tasks using customized generative AI processing techniques. The system automatically pre-fills missing fields from structured and unstructured data, completes data fields that are required for underwriting data processing, validates existing fields from submissions, scores submitted data for completeness, and determines whether the data is in condition for submission to an insurance carrier for processing. The system also provides a conversational AI chat interface which allows underwriters to ask questions of the system as information is being processed. The system accelerates processing of underwriting data and uncovers patterns in data that can be used to refine future decision-making and/or processes.

In certain aspects, a method includes receiving patient information of a patient including payor information. The method includes determining, based on the payor information, a primary payor and at least one secondary payor. The method includes determining a submission deadline date for each payor that were determined. The method includes transmitting a medical claim to a primary payor device associated with the primary payor. The method includes determining an advance submission date for each secondary payor, and assigning the advance submission date to each secondary payor. The method includes monitoring a healthcare provider device for payment data related to the medical claim. The method includes monitoring each advance submission date previously assigned to each secondary payor. The method includes transmitting, based on determining, from the payment data monitored, that there is a balance amount remaining on the medical claim, a secondary medical claim to a secondary payor device.

Systems and methods are disclosed with respect to using a distributed ledger, such as a blockchain, for managing loss histories for individuals and/or assets, such as real estate, homes, and vehicles, and/or for creating and maintaining loss history reports for individuals and/or insurable assets. The loss histories or loss history reports may be individual or insured specific, or may be insurable or insured asset specific.

An automatic insurance claim denial management system and process are disclosed. The automatic insurance claim denial management method receives a notification of denial of the insurance claim by one or more payers. The notification also includes an Electronic Remittance Advice (ERA) reconciliation data provided by the payer. One or more reasons for claim denial are automatically identified by analyzing the ERA data. After analysis of the ERA data and knowing one or more reasons for the denial of the insurance claim, corrections are applied to an insurance claim form i.e., rejected by the payer. Finally, the modified insurance claim form is submitted to the payer, ensuring that the insurance claims meet the necessary criteria for approval upon re-submission.

An automatic bulk insurance claims re-submission of pending insurance claims facilitates transitioning from one technical electronic medical record (EMR) system to another EMR system. The method involves extracting past insurance claims that remain pending due to non-submission or rejection by one or more payers for various reasons, classifying these as pending insurance claims. It further includes analyzing Electronic Remittance Advice (ERA) reconciliation data provided by the payer to identify discrepancies between expected and actual payments. The payment status and reasons behind non-payment for each pending claim are determined. Necessary modifications are then implemented to ensure that the claims meet approval criteria. Finally, the method automatically resubmits all modified pending claims in a single bulk upload, ensuring that all corrected claims are accurately reflected in the submission, and ensuring that the efficiency and accuracy of the insurance claim re-submission process are maintained, leading to improved reimbursement outcomes for healthcare providers.