Kaggle Involvement Program

2018 Kaggle Involvement Program Winners

The Society of Actuaries (SOA) is pleased to recognize the winners of the 2018 Kaggle Involvement Program. SOA members participated in data science competitions that challenged them to use cutting-edge technology to build models and find solutions with important societal implications, from preventing environmental accidents at sea to ensuring underserved populations have access to fair lending practices. During the 2018 program, 11 members placed in the top 10 percent of their challenges. Additionally, Carlos Brioso, FSA, CERA, achieved the coveted title of Kaggle Competitions Master. As a seasoned Kaggler, Carlos' success in previous challenges, along with his finish in the top one-percent of participants in the Home Credit Default Risk competition, earned him a spot among this elite group of data scientists.

Kaggle competitions are filled with thousands of participants from around the world and the continued success of SOA members in the Kaggle program is proof that actuaries are truly the data science experts that industries seek. Congratulations to all participants in this year's program!

Kaggle Competitions Master

The Kaggle Progression System allows participants to earn medals for achievements and compete for data science "glory" on live leaderboards. There are five performance tiers that can be achieved in accordance with the quality and quantity of work produced in Kaggle competitions: Novice, Contributor, Expert, Master, and Grandmaster. Learn More

* Rank as of 2/6/2019

Competition: Airbus Ship Detection Challenge

Airbus challenged Kagglers to aid in building a model that detects all ships in satellite images as quickly as possible to prevent infractions at sea such as environmentally devastating ship accidents, piracy, illegal fishing, drug trafficking, and illegal cargo movement. Learn More

*After the Airbus Ship Detection Challenge ended, competitors were able to submit their model to Airbus for a speed evaluation based upon the inference time on over 40,000 image chips. Maria’s model placed 8^th out of 100 entries in this additional challenge.

Competition: Home Credit Default Risk

Using various statistical and machine learning methods to make these predictions, Home Credit Group challenged Kagglers to help unlock the full potential of their data to ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful. Learn More

Competition: Santander Value Prediction Challenge

In this competition, Santander Group asked participants to assist in improving personalized customer service by using data to identify the value of transactions for each potential customer. Learn More

Competition: TGS Salt Identification Challenge

Several areas of Earth with large accumulations of oil and gas also have huge deposits of salt below the surface. To find these deposits, TGS asked Kaggle’s machine learning community to build an algorithm that automatically and accurately identifies if a subsurface target on a seismic image is salt or not. Learn More