Repository for quests.kasa.ai which maintains a listing of current projects.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
Adventures in research at the intersection of insurance and AI
Home Page: https://quests.kasa.ai
Repository for quests.kasa.ai which maintains a listing of current projects.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
Automatically estimate damages from roof pix
There is quite a bit of information on P&C ratemaking available, e.g. the Werner and Modlin text and the CAS monograph on GLM; however, there doesn't seem to be a tutorial for doing a ratemaking project end-to-end with R. The goal of this proposed project is to produce a tutorial or series of blog posts that emulates what actuaries do (at least the technical bits) in a typical pricing project, from preparing raw data to filing.
This work will be conducted in a public GitHub repo and open to contributions throughout.
Currently recruiting
This project investigates using machine learning methods to predict shock lapses at the end of the level term in term life insurance for assumption setting and risk management. Methods from GLM to deep neural networks will be benchmarked. Recruiting is complete and work is underway. The repository will be made public when the first preprint is available in January.
This proposed task would evaluate the actuarial and insurance journals and make recommendations as to where to send papers. Things to consider may include
The deliverable should be a document that will live in this repo, and will inform researchers in making tradeoffs. E.g. anecdotally, Variance is read by more practitioners in the US but has a huge backlog of accepted but not yet published papers; MDPI's Risks is open access and has faster turnaround but has less prestige than IME which may take months for each review round, etc.
Looking for someone to take the lead on this.
Workers Comp Rating Plans vary by state. Many states use the NCCI rating plan, but others have their own bespoke plans. They all share some common features. My proposal is the to create a unified R interface to these rating plans so that actuaries can
I started to collect information to do this several years ago, but I didn't get very far: wcratr
The README
is way out of date and needs a revamp. Maybe this is better off as a simple web page deployed from the repo. Will get something up in the next week.
It is widely accepted that modern machine learning (ML) techniques, such as gradient boosted trees and deep neural networks, have better predictive performance than traditional predictive modeling techniques such as generalized linear models (GLM.) However, one major obstacle for moving beyond GLM in pricing is the perceived lack interpretability of the ML techniques. This project will investigate techniques for model interpretability in the context of regulated environments in the US.
The deliverable of this project will include a research paper and potentially contributions to existing open source packages.
Currently recruiting
Create tool (website?) that leverages google and/or microsoft's OCR API to extract loss triangle data out of pdfs. A tool to extract data from loss runs would also be useful, but the geometry of loss runs seen in the wild are much more varied.
I want to get all of my thoughts out here. This is probably multiple inter-related projects.
Steps:
Also want to highlight:
This is meant to be pretty general but let's take an underwriting model for a personal auto carrier as an example. The starting point is a trained u/w score model and decision rules on whether to accept/deny/defer to human an auto insurance application. We want to expose this model as a service so apps/websites can hit it.
This is impactful/important because we need to demystify the operationalization process, and data scientists (incl. actuaries) should be familiar with the entire stack even if their day-to-day isn't orchestrating containers.
Initial assumptions/requirements
Results
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.