Predictive Analytics

Many insurers have significant potential to generate lift by aligning rates more closely with underlying risk levels.

When it comes to liability claims, managers want fast, fair, and consistent resolutions that keep loss costs under control.

Verisk studies show by-peril rating enhances market share and profitability for homeowners insurers.

Verisk analysis shows how to improve and reduce the cost of the customer acquisition process, for both the consumer and the insurer.

Verisk assessment of hail damage potential using the Hail Damage Score™ reveals top 10 riskiest states for hail damage exposure.

Survey finds majority of large homeowners insurers are incorporating by-peril rating structures, predictive analytics to more closely align premium to risk.

AIR Worldwide released a probabilistic inland flood model for the United States to provide underwriters tools for assessing and managing inland flood risk.

The ISO Risk Analyzer in 360Value helps underwriters assess property-specific peril risks to better project replacement cost estimates.

Hail is a major risk management priority for property insurers, and the new analytics from Verisk help insurers manage hail risk more effectively.

Advancements in homeowners data and analytics and improved insight into risk are increasing insurer confidence in underwriting.

The predictive analytics functional process is complex. Claims, carriers, TPAs, and self-insureds of all sizes must follow a series of steps before they can accurately…

Workers' compensation claim departments have long struggled to contain excessive costs — typically driven by a small minority of difficult cases.

By collecting and using data from multiple sources, insurers can segment risks to develop profitable, competitive insurance policies.

There are three primary strategies that insurers can implement to manage the risk of future hailstorm losses.

Industry estimates put total auto insurance losses due to fraud and rate evasion between $12 billion and $24 billion annually.