Making Machine Learning Work for Insurers
Deborah Koens from Amdocs Studios shares actionable insights on the effective implementation of machine learning frameworks.
Insurers hold vast reserves of valuable data, including claims figures, risk model outputs, customer behavior analytics, and insights from wider business functions. Machine learning (ML) can unlock value from this data at scale. Yet only a small percentage of ML models developed by industry ever get deployed.
Challenges often centre on issues with data quality and governance or model bias and underperformance. These matters are dealbreakers in highly regulated sectors like insurance. Many businesses are actively experimenting with ML, but few manage to progress concepts into fully functioning solutions.
The true frontier is not simply adopting ML but making it operational at scale. And this is where ML operations (MLOps) comes to the fore.
This article was first published on Insurance Edge
