Popular GitHub Repositories for Claim

Links to conference/journal publications in automated fact-checking (resources for the TACL22/EMNLP23 paper).

NotPixel automatic claim and paint bot? easy to use without extras.

Auto chat, leave, join discord server, delete, reaction messages with AI. [100 STARS TO ENABLE AUTO CHAT WITH AI]

This project aims to emulate some of the advanced reasoning capabilities seen in models like OpenAI's o1. It does not claim to replicate the exact functionality or performance of o1.

Fact-checking system for textual and visual inputs.

Auto connect wallet, referrals, chat with AI.

Automation for Pixai.art auto claim credits

An intelligent full-stack insurance claim decision-making system that combines FastAPI backend with React TypeScript frontend, powered by Hugging Face Transformers and Gemma LLM. This AI agent analyzes insurance clauses and customer queries to predict whether a claim should be Approved or Rejected, along with justification and estimated amounts.

Auto do tasks, play games, referrals, checkin.

Insurance claim fraud detection using machine learning algorithms.

GLM, Neural Network and Gradient Boosting for Insurance Pricing, Part 1: Claim Frequency

Successfully established a machine learning model which can estimate the net health insurance claim of an individual based on a set of characteristics of that individual to an appreciable level of accuracy.

A retrieval-augmented generation-based framework for automatically constructing a hierarchy of aspects typically considered when addressing a nuanced claim and enriching them with corpus-specific perspectives.

The platform features: Provide 24/7, personalized guidance on policy selection and claims via voice or chat. Ensure clear understanding of policy terms. Automate form-filling with Al for error-free applications and higher claim acceptance rates.

?? Accident damage estimation for insurance companies using the CatBoost algorithm. ?? This advanced technique utilizes machine learning to provide accurate estimates for assessing damages caused by accidents. ?? Enhance efficiency and accuracy in insurance claim processes with the power of CatBoost! ??

Building a ML model to predict whether the customer will apply for the claim or not with deployment.

To build a classification methodology to determine whether a customer is placing a fraudulent insurance claim.

Different models to detect if a claim is fraudulent or not

? A dynamic pricing and insurance risk modeling system using Python, XGBoost, SHAP, and DVC. Predicts claim severity and probability, enabling risk-adjusted premium strategies with full reproducibility and CI/CD.

Verify claims using AI agents that debate using scraped evidence and local language models.