A machine learning based (ML-based) method and system for processing claims in a claim decision readiness ecosystem for first users is disclosed. The ML-based method comprises obtaining data in view of first forms associated with the claims from communication devices associated with first users; categorizing the first forms into claim type and claim characteristics documents, regulatory requirement documents, and insurance carrier business rule documents; generating claim decision readiness scores based on receipt, non-receipt, completeness, and incompleteness, of the categorized first forms and associated data fields, using a claim decision readiness scoring tool; executing automated workflow channels based on the generated claim decision readiness scores with pre-defined business rules; validating data in the data fields using redundant and repetitive questions across the categorized first forms; updating the claim decision readiness scores and the automated workflow channels, to adjudicate the claims.

This invention relates to a comprehensive tool for simulating, analyzing, and reporting on permanent life insurance policy performance under various economic conditions, insurance companies, and stakeholder and policyholder behaviors. The tool enables the creation of baseline policy illustrations and various financial models incorporating life insurance, validates projections against known and hypothetical data, and simulates future scenarios to provide actionable insights for policyholders and advisors.

The following relates generally to AI-based review of insurance claims complaints. In some embodiments, one or more processors: (1) receive, via a chatbot, an insurance claim complaint; (2) categorize, via the chatbot, the insurance claim complaint by determining a category of the insurance claim complaint, the category comprising a tone category or a policy category; (3) build, via the chatbot, a complaint report including information of the insurance claim complaint and an indication of the category; and/or (5) send, via the chatbot, the complaint report to an insurance complaint administrator computing device.

A data processing system implements a system for training and fine-tuning a plan recommendation model that recommends one or more insurance plans for a user based the cost of the insurance plans and the needs of the user. The plan recommendation model is trained and/or fine-tuned using training data that has been labeled by a human actuary to improve the accuracy of the model. The plan recommendation model is also fine tuned based on feedback received on recommendations made by the model to further improve the performance of the model.

A method includes receiving an indication that a record has been updated and determining a confidence score. The method includes, in response to a determination that the confidence score has not met a threshold, filtering a set of communications. The method includes determining whether the record change has met one or more of a set of validity criteria. The method includes, in response to a determination that the user record change has not met the set of validity criteria, based on a first outcome of a set of reporting criteria, automatically generating a prompt with a first suggested revision of the first change. The method includes, in response to a determination that the record change has not met the set of validity criteria, based on a second outcome of the set of reporting criteria, automatically revising the first change with the first suggested revision.

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.

A method includes receiving an indication that a record has been updated and determining a confidence score. The method includes, in response to a determination that the confidence score has not met a threshold, filtering a set of communications. The method includes determining whether the record change has met one or more of a set of validity criteria. The method includes, in response to a determination that the user record change has not met the set of validity criteria, based on a first outcome of a set of reporting criteria, automatically generating a prompt with a first suggested revision of the first change. The method includes, in response to a determination that the record change has not met the set of validity criteria, based on a second outcome of the set of reporting criteria, automatically revising the first change with the first suggested revision.

A robotic insurance platform is disclosed that transforms an autonomous service robot into a tamper-resistant digital witness capable of supplying legally probative evidence without human intervention. The robot is equipped with surround video cameras, a spatial microphone array, an on-board processor, a cryptographically isolated secure enclave, a wireless communication module, and a rolling-buffer memory that retains encrypted audio-video data for a configurable period such as forty days. A companion mobile application enables an insured user to register, perform know-your-customer identity verification, and pair the robot with an insurance policy stored on a cloud server that hosts an claim-decision engine and policy database. Following explicit verbal consent from the user, the robot records continuously while performing ordinary tasks. When an incident is detectedeither by on-board heuristics or by a user-initiated claim requestthe processor extracts a time window surrounding the event, computes a cryptographic hash of the clip inside the secure enclave, and commits that hash as an immutable anchor to a permissioned or public blockchain ledger. Only after blockchain confirmation is the encrypted clip transmitted to the insurance server, where the claim-decision engine verifies integrity, applies machine-learning analytics to determine causation, and issues a coverage determination. Approved claims trigger repair dispatch, replacement shipment, or direct monetary reimbursement, while unclaimed data exceeding the retention interval are securely erased. The platform delivers objective, bias-free evidence, virtually eliminates false claims, and reduces end-to-end settlement time from weeks to minutes, thereby lowering operational costs for insurers and increasing transparency for policyholders.

An information processing apparatus includes an acquisition unit that acquires notification information indicating a candidate item that may be associated with a notification item in an insurance contract, and an extraction unit that extracts a notification item related to the candidate item from a document in which the notification item of the insurance is described, using an extraction model. The information processing apparatus enables an applicant to be supported in making a decision in reporting a notification item.

This disclosure relates generally to method and system for identifying frauds in unemployment insurance claims using hybrid weighted decision model. The method combines predictive power of machine learning models with domain knowledge infused key fraud indicators and network analysis to identify false positive claims. Initially, a set of claim information from an request of a claimant is extracted to assess risk affecting eligibility of the claimant to receive benefits. Further, a classification probability for a set of key fraud indicators are predicted for each claim using a set of top features associated with a prescient artificial intelligence (AI) model. Finally, one or more frauds associated with the unemployment insurance claim of the claimant are identified based on the weighted probability of each claim, a network diagram generated using the weighted probability for each claim, a set of filtered claims, and a set of business rules.