Inspiration
The process of understanding whether insurance companies would accept a customer's case can be time-consuming and overwhelming for many. We wanted to create a simple and user-friendly method to help individuals quickly determine their eligibility for insurance coverage while also identifying possible biases in the decision-making process.
What it does
UClaim is an innovative platform that uses advanced machine learning algorithms based to accurately predict whether an individual is likely to be accepted by an insurance company. Users can input their personal information, and the system analyzes this data to provide a probability of claim acceptance while also identifying any potential biases that may be affecting the decision.
How we built it
We developed UClaim using a multi-layer perceptron (MLP) and transformer model that has been trained on a dataset of various demographics and case acceptance rates. The platform utilizes this model to classify users based on their provided information, predicting whether their case will be accepted or not. We also implemented a sensitivity analysis to identify any biases that may be present in the decision-making process.
Challenges we ran into
Building an accurate and reliable model for predicting insurance claim acceptance was a significant challenge, as we needed to ensure that the algorithm could analyze various factors and provide an appropriate outcome. Additionally, identifying and addressing biases within the system presented challenges as we tried to ensure that the platform offers fair and unbiased results.
Accomplishments that we're proud of
We are proud of creating a user-friendly platform that simplifies the process of understanding insurance claim eligibility. The system not only provides accurate predictions but also aims to expose and address biases in the decision-making process. We believe that this will help users make informed decisions and ultimately improve their chances of securing appropriate insurance coverage.
What we learned
Throughout the development of UClaim, we learned the importance of accurate data analysis and how biases might impact the outcome. We also gained valuable insights into the world of insurance claims and how complex the decision-making process can be.
What's next for UClaim
As we continue to develop and expand UClaim, we plan to incorporate additional datasets and refine our algorithm to increase accuracy and reliability. We also aim to address potential biases further and ensure that our platform remains as fair and unbiased as possible, helping users secure the insurance coverage they need. We want to gear our website towards both user and enterprise solutions.
Built With
- ai
- ai-app
- ai-applied-data-miner
- ai-applied-demographics
- ai-applied-language-detection
- ai-applied-sentiment-analysis
- ai-applied-text-extract
- ai-applied-text-label
- ai/ml
- anaconda
- analysis
- api
- api-call
- artificial-intelligence
- artificial-intelligence-machine-learning
- backend
- bar-graph
- cable
- chakra
- cleaning
- cmd
- coffee
- comma-separated-file
- computer
- conda
- css
- csv
- data
- data-analysis
- data-cleaning
- dataset
- devpost
- dplyr
- eslint
- excel
- flask
- frontend
- ggplot
- ggplot2
- github
- github-jobs
- gpt
- gpt-3.5
- gpt-4
- html
- hugging-face
- ipynb
- javascript
- json
- jupyter
- jupyter-lab
- jupyter-notebook
- kaggle
- learn
- learning
- machine-learning
- matplotlib
- ml
- mlp
- model
- multi-layer-perceptron
- natural-language-processing
- neural-network
- next
- next-app
- next.js
- nn
- node.js
- npm
- numpy
- openai
- outlet
- pandas
- parsing
- pip
- plotly
- png
- poetry
- pwa
- python
- pytorch
- r
- re
- react
- react.js
- read-the-docs
- regex
- regexlib
- rest
- rest-api
- restful
- restful-api
- rstudio
- tableau
- tailwind
- tensorflow
- terminal
- text-parsing
- tidyverse
- transformer
- transformers
- txt
- typescript
- ui
- vercel
- vercel-app
- visualization
- vscode
- web
- web-app
- web-dev
- web-development
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