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https://github.com/jimbrig/lossrunAnalyzer

R Package and Shiny App to Analyze Insurance Lossruns
https://github.com/jimbrig/lossrunAnalyzer

actuarial data-analysis data-mining data-science insurance r record-linkage risk-management shiny

Last synced: 9 days ago
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R Package and Shiny App to Analyze Insurance Lossruns

Host: GitHub
URL: https://github.com/jimbrig/lossrunAnalyzer
Owner: jimbrig
Archived: true
Created: 2020-01-10T20:01:18.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2020-01-10T20:52:20.000Z (almost 5 years ago)
Last Synced: 2024-08-13T07:14:15.411Z (4 months ago)
Topics: actuarial, data-analysis, data-mining, data-science, insurance, r, record-linkage, risk-management, shiny
Language: R
Size: 11.7 KB
Stars: 4
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.Rmd

Awesome Lists containing this project

jimsghstars - jimbrig/lossrunAnalyzer - R Package and Shiny App to Analyze Insurance Lossruns (R)

README

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```

# lossrunAnalyzer - Analyzing Insurance Claims Data

The goal of **lossrunAnalyzer** is to assist actuaries to quickly analyze,
diagnose, and summarize lossruns containing individual claims data for
property casualty insurance.

## Badges

[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
[![Project Status: WIP](https://www.repostatus.org/badges/latest/wip.svg)](http://www.repostatus.org/#wip)

## Installation

You can install lossrunAnalyzer from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("jimbrig2011/lossrunAnalyzer")
```

## Roadmap

The end-goal of **lossrunAnalyzer** is to provide support for the following:

- Initial Reasonability Checks:
+ Unique claim ID
+ Paid + Case = Incurred
+ Totals = Sum of Splits
+ Report Date >= Loss Date
+ Field Consistency (i.e. States, Status, etc.)

- Possible Duplicate Detection

- Occurrence Grouping

- Adding "Working" Fields
+ Retentions / Limits / Deductibles
+ Various "Limited" Amounts
+ ALAE Treatments
+ Years (Policy, Accident, Report, Fiscal, Calendar)
+ Lags (Report, Close, Tenure)
+ Max IBNR's at various scenarios
+ Legal, Lost-Time, Indemnity Support

- Utilizing Lookup / Support Tables

- Record Linkage to Reduce Fuzzyness

- Merging Lossruns Across Multiple Evaluations

- Comparing Lossruns to Prior's and Addressing Various KPI's / Diagnostic Checks

- Anomaly / Outlier Detection

- Automating Development Comments as to why things changed

- Summarizing Data into Triangles

- Performing an AvE (Actual vs. Expected) Analysis Summary

- Checking for Dropped / Missing Claims or New Claims with Old Dates

- Tie Out to Exposures