How Horse Racing Predictions with H2O.ai Saved a Local Insurance Company $8M a Year

Name: How Horse Racing Predictions with H2O.ai Saved a Local Insurance Company $8M a Year
Uploaded: 2022-12-04
Duration: 16 min
Description: Simply AI's Matthew Foster on predicting the Melbourne Cup winner with H2O AutoML, and carrying that approach into an insurance company's Snowflake work.

Predictive AI
H2O Driverless AI
H2O Hydrogen Torch

Overview

Matthew Foster, chief data scientist at Simply AI, traces his path from a failed rules-based system to predicting the winner of the Melbourne Cup with an H2O AutoML model built on roughly 1,000 features per horse. He then shows how that same approach carried into enterprise work, describing an insurance company's Snowflake-integrated claims prediction model built with H2O, plus a Hydrogen Torch NLP project that labels and searches tens of thousands of unstructured policy documents through an H2O Wave app.

Key takeaways

How the horse racing model used H2O AutoML (built years before Driverless AI existed) with around 1,000 features per horse, including trainer accuracy, jockey history, and even the racing form of a horse's parents.
How, for an insurance company client, preprocessing and scoring happen inside Snowflake: a Driverless AI model (MOJO) is uploaded and scored there via a UDF, with results piped to a postprocessing step used for monitoring and dashboards.
Why the presenter calls the claims-prediction model's savings, an estimate of around $2 million per year, conservative, noting the real number could be higher.
The team's three operating principles: simplify (they removed about 40 percent of the preprocessing code with no loss in accuracy), automate (auto-generating and scheduling test cases across environments), and monitor (watching daily for model and data drift).

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