How Machine Learning is Transforming Insurance Claims

Machine Learning as been one of the most prolific areas when it comes to high-impact use cases for the insurance industry. And within insurance, claims management offers one of the most promising areas to apply this technology due to the large amount of data available to train algorithms and the consistency of principles applied in the claims assessment process.

Here, we look at some of the use cases for ML in claims and challenges limiting adoption.

Fraud detection is perhaps the area where we see the most advanced adoption of ML among insurance companies. Start-ups such as Shift Technology, Friss and Owl Labs have seen strong demand from carriers and attracted significant capital from investors to support their growth.

These tools function by applying cutting-edge data science to large historical claims data sets, enriched with third-party dataInsurers have been quick to adopt ML-based fraud detection strategies because they can often deliver the most immediate and tangible return on investment.

Many fraud teams within claims rely on rules-based approaches (often inside an adjuster’s head) and tend to focus on those claims where the scope for fraud is most obvious, potentially neglecting larger, more complex fraud cases.