How machine learning will spark a revolution in insurance
Siddhartha Dalal got his introduction to probabilistic analysis in the wake of the 1986 Space Shuttle Challenger disaster. Dalal’s research on behalf of the National Academy of Sciences found that NASA’s estimates of a 0.5 percent risk of the o-ring gasket failure that caused the explosion was dramatically off-target. At the 31-degree Fahrenheit air temperature on the morning of the launch, the risk was more than 16 percent. In other words, the Challenger lifted off with a one-in-six chance of exploding. “There was no evidence of failure because 24 flights had happened without incident,” he told the MIT Chief Data Officer and Data Quality Symposium on Thursday, “but there had been partial failures that could have formed a better statistical base.” In fact, that data would have shown a clear correlation between temperature and o-ring failure. As chief data scientist and senior vice president at American International Group Inc., Dalal is now applying technologies like computer vision, natural language processing and sensors to probabilistic risk analysis. Big data, he said, is transforming the discipline of insurance underwriting. “The insurance industry all about understanding risk,” he said. “The last four years have been amazing in the advances we’re making.” The discipline of risk…
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How machine learning will spark a revolution in insurance
