How Insurers Are Applying Machine Learning

Just like financial institutions, insurers are no strangers to leveraging advanced technologies in various aspects of the business. Some of the practical applications of machine learning in the insurance industry include managing broker business, optimizing direct marketing, understanding quote conversion, computing optimal pricing, detecting fraud, claims triage, predicting litigation, targeting inspections and audits, forecasting claims, retaining customers, and, finally, recalibrating prices. Extensive research by Satadru Sengupta, General Manager & Data Scientist, Insurance at DataRobot, explores particular ways machine learning can impact operational efficiency. Let’s take a closer look at some interesting examples and partnerships.

Claims forecasting

Insurance executives need accurate loss predictions so that they can set reserves appropriately. Machine learning delivers the high-quality predictions insurers need for smart decisions.

Actuaries and statisticians have used historical data to recognize patterns in claims and predict future losses for over 100 years. They’ve been pretty creative in doing so, using tools in line with the technology of their time from minimum bias all the way up to decision trees. The level of sophistication and tools has changed over time, and I look at Machine Learning and AI as transformative for the way we try to solve the same problems while also gaining insights from places where traditional methods fail,

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