fychf

fychf's Stars

• henckr/treeML

  Boosting insights in insurance tariff plans with tree-based machine learning methods

  Language:HTML87

• actuarial-data-science/Tutorials

  Code for the Actuarial Data Science Tutorials published at https://actuarialdatascience.org.

  Language:HTML181127

• abdullahkarasan/mlfrm

  Language:Jupyter Notebook7650

• The-Run-Philosophy-Organization/run

  润学全球官方指定GITHUB，整理润学宗旨、纲领、理论和各类润之实例；解决为什么润，润去哪里，怎么润三大问题； 并成为新**人的核心宗教，核心信念。

  31.9k2.6k

• Numer1301/Capstone-Project-on-Insurance-claims

  India is a huge market for insurance but the industry is bleeding losses due to increasing fraud from inside and outside of the system. Insurance frauds leads to close to INR 40,000 crores loss which is close to 8.5% revenue of the insurance sector in India. These huge losses have prompted insurance companies to set-up a new anti-fraud department whose job is to identify the risk and loss to these frauds and to find out ways to reduce fraudulent claims. We now have a data from an insurance company (Name not disclosed) which is losing revenue in fraudulent claims. Based upon the given data build the below models: • Predict if the DRV_CLAIM_STATUS will be 'Rejected' or 'Accepted'. Consider 'Closed' status given in data as 'Accepted' status. This prediction will help the Claims Management team to manage the claims effectively. Prediction model will give them the list of claims to be rejected in advance thus helping them to scrutinize these claims and ensure no leakage in for of frauds. • Identify clusters/associations in the claims data to classify them based on the severity of the claims. Identify fields that are classifying the claims as highly severe claims. These will be the claims that will need more focus and scrutiny from the fraud management team.

  Language:R2

• Deepak-Sathyanarayanan/Analyzing-Predicting-Health-Insurance-Fraud-Detection-Dataset

  Language:Jupyter Notebook1

• IBM/analyzing-insurance-claims-using-ibm-db2-graph

  Analyzing insurance fraud claims using IBM Db2 graph database

  Language:Jupyter Notebook125

• dchannah/fraudhacker

  Anomaly detection system for medical insurance claims data

  Language:Jupyter Notebook157

• AvinashSingh786/Fraud-Analysis

  Insurance fraud claims analysis project

  Language:Python5228

• robbertliu/deeplearning.ai-andrewNG

  deeplearning.ai , By Andrew Ng, All slide and notebook + data + solutions and video link

  Language:Jupyter Notebook1.5k558

• casact/rp-bnn-claims

  Individual Claims Forecasting with Bayesian Mixture Density Networks

  Language:TeX153

• A1arcon/R_Actuarial

  El objetivo de este repositorio es brindar un apoyo a la comunidad interesada en mejorar sus técnicas en el lenguaje de programación R o emprenderlo desde un punto de vista muy aplicado. Un repositorio con códigos de R para aplicaciones actuariales: probabilidad, estadística, riesgo y finanzas.

  Language:R8713

• BruceZhaoR/Zh-beamer

  Template for creating Chinese beamer & PDF on Windows

  Language:TeX3830

• JoaquinAuza/DetLifeInsurance

  Life Insurance Premium and Reserves Valuation

  Language:R107

• tianiachan/Healthcare_Fraud

  Delving into prescription drug records and using Machine Learning to predict what providers are most likely fraudulent.

  Language:Jupyter Notebook4

• fychf/CMS-Fraud-Detection

  Fraud detection algorithm using Autoencoders and Stacked Autoencoders to detect fraudulent physicians in CMS Part B claims data

  1

• hetrone/kesai-anti-fraud

  人工智能社会保险反欺诈分析

  Language:Jupyter Notebook308

• marrygit/shebao-fanqizha

  这是由南京市人民政府主办的2018全球（南京）人工智能应用大赛的一部分，社会保险反欺诈分析。记录一下分析过程

  Language:Jupyter Notebook1

• doodzhou/OSFS

  Online Streaming Feature Selection

  Language:MATLAB278