Detailed analysis on fraud claims in insurance companies, gives you information as to why huge loss take place in insurance companies

Overview

Insurance-Fraud-Claims

Detailed analysis on fraud claims in insurance companies, gives you information as to why huge loss take place in insurance companies

Introduction ( Purpose )

Fraud detection occurs in many industries such as banking and financial sectors , insurance , healthcare and more. Upcoding fraud in recent years has risen sharply where fraudsters come up with different ideas to claim a financial gain through insurance claims . In Upcoding fraud by claiming more amount than the usual costs for their service. Incorporating artificial intelligence with data mining and statistics help decrease these kinds of frauds. Data mining is used to scale huge transactions and detect the fraudulent ones whereas the hybrid learning methodology helps detect frauds.

The primary incentive to commit upcoding is financial gain. Upcoding appears in different ways such as Upcoding of services, Upcoding of items and Duplicate claims. Data mining helps detect such fraudulent claims in the future. It also increases an adjuster’s efficiency by narrowing down prospective audits and Identifies and isolates factors that indicates potential fraudulent activity.

REQUIREMENTS

--> Functional Requirements

  1. The model should be able to detect the fraud transactions .

--> Non Functional Requirements

  1. The accuracy of the predicted value must be precise.
  2. The model should never fail in the middle of operation.
  3. The model should work consistently across various platforms.

--> Software Requirements

  1. OS Version: Windows 7(64 bit) or newer versions.
  2. Coding Language: Python 3.6
  3. Platform: Jupyter Notebook

--> Hardware Requirements

  1. Processor: i5 or i7 Intel Processor
  2. Primary Storage: 8 GB RAM or above (Recommended 16 GB)
  3. Secondary Storage: Any standard HDD or SDD
Hg002-qc-snakemake - HG002 QC Snakemake

HG002 QC Snakemake To Run Resources and data specified within snakefile (hg002QC

Juniper A. Lake 2 Feb 16, 2022
Repositori untuk menyimpan material Long Course STMKGxHMGI tentang Geophysical Python for Seismic Data Analysis

Long Course "Geophysical Python for Seismic Data Analysis" Instruktur: Dr.rer.nat. Wiwit Suryanto, M.Si Dipersiapkan oleh: Anang Sahroni Waktu: Sesi 1

Anang Sahroni 0 Dec 04, 2021
A Big Data ETL project in PySpark on the historical NYC Taxi Rides data

Processing NYC Taxi Data using PySpark ETL pipeline Description This is an project to extract, transform, and load large amount of data from NYC Taxi

Unnikrishnan 2 Dec 12, 2021
The official repository for ROOT: analyzing, storing and visualizing big data, scientifically

About The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data in a very efficien

ROOT 2k Dec 29, 2022
Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities. This is aimed at those looking to get into the field of D

Joachim 1 Dec 26, 2021
Statistical Rethinking course winter 2022

Statistical Rethinking (2022 Edition) Instructor: Richard McElreath Lectures: Uploaded Playlist and pre-recorded, two per week Discussion: Online, F

Richard McElreath 3.9k Dec 31, 2022
Creating a statistical model to predict 10 year treasury yields

Predicting 10-Year Treasury Yields Intitially, I wanted to see if the volatility in the stock market, represented by the VIX index (data source), had

10 Oct 27, 2021
Project: Netflix Data Analysis and Visualization with Python

Project: Netflix Data Analysis and Visualization with Python Table of Contents General Info Installation Demo Usage and Main Functionalities Contribut

Kathrin Hälbich 2 Feb 13, 2022
Jupyter notebooks for the book "The Elements of Statistical Learning".

This repository contains Jupyter notebooks implementing the algorithms found in the book and summary of the textbook.

Madiyar 369 Dec 30, 2022
Open-source Laplacian Eigenmaps for dimensionality reduction of large data in python.

Fast Laplacian Eigenmaps in python Open-source Laplacian Eigenmaps for dimensionality reduction of large data in python. Comes with an wrapper for NMS

17 Jul 09, 2022
A model checker for verifying properties in epistemic models

Epistemic Model Checker This is a model checker for verifying properties in epistemic models. The goal of the model checker is to check for Pluralisti

Thomas Träff 2 Dec 22, 2021
The Spark Challenge Student Check-In/Out Tracking Script

The Spark Challenge Student Check-In/Out Tracking Script This Python Script uses the Student ID Database to match the entries with the ID Card Swipe a

1 Dec 09, 2021
A distributed block-based data storage and compute engine

Nebula is an extremely-fast end-to-end interactive big data analytics solution. Nebula is designed as a high-performance columnar data storage and tabular OLAP engine.

Columns AI 131 Dec 26, 2022
Data collection, enhancement, and metrics calculation.

l3_data_collection Data collection, enhancement, and metrics calculation. Summary Repository containing code for QuantDAO's JDT data collection task.

Ruiwyn 3 Dec 23, 2022
Analysis scripts for QG equations

qg-edgeofchaos Analysis scripts for QG equations FIle/Folder Structure eigensolvers.py - Spectral and finite-difference solvers for Rossby wave eigenf

Norman Cao 2 Sep 27, 2022
Projects that implement various aspects of Data Engineering.

DATAWAREHOUSE ON AWS The purpose of this project is to build a datawarehouse to accomodate data of active user activity for music streaming applicatio

2 Oct 14, 2021
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilis

Blei Lab 4.7k Jan 09, 2023
Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

Amundsen 3.7k Jan 03, 2023
Maximum Covariance Analysis in Python

xMCA | Maximum Covariance Analysis in Python The aim of this package is to provide a flexible tool for the climate science community to perform Maximu

Niclas Rieger 39 Jan 03, 2023
MS in Data Science capstone project. Studying attacks on autonomous vehicles.

Surveying Attack Models for CAVs Guide to Installing CARLA and Collecting Data Our project focuses on surveying attack models for Connveced Autonomous

Isabela Caetano 1 Dec 09, 2021