InsurSight

Automated loss reserving

InsurSight automatically sets first-cut loss reserves for every reserving class and cohort in minutes.

InsurSight automatically sets the assumptions used in traditional loss reserving approaches. For example, pattern averaging periods, method selection etc.

InsurSight also shows you where to prioritize your time, using the ‘universe of reserves^™’ to show where there is the most uncertainty in the automatically selected reserves.

Key assumptions prioritized

InsurSight shows you which reserving assumptions are the most important for each loss reserving class.

This gives you clarity on which assumptions to focus on right at the start of the loss reserving process, giving more time for wider business input on key judgments.

Easy-to-review diagnostics

Easy-to-review data visualizations of key assumption selections and diagnostics are set up automatically right at the start of the process.

1. For each key assumption, the selection made is clearly shown alongside alternatives that could have been used
2. Reserving diagnostics used by regulators, such as weakening of case estimates, are embedded into the loss reserving process
3. Dashboards are fully customizable, set up in seconds, and highlight the key features of each loss reserving class

Assumption fine-tuning

Easily adjust any assumption set automatically by InsurSight, and fine-tune specific assumptions where necessary. For example:

1. For development patterns you can: make exclusions, set averaging periods, set tail assumptions, and set patterns by cohort
2. For initial expecteds you can: on-level past Ultimate Loss Ratios (ULRs) for rate change and inflation, use various trending approaches, and adjust weights used in averages for specific cohorts (eg where there are unusually large or small losses)
3. For method selection you can: apply custom weights by cohort to a range of methods across paid, incurred, frequency-severity, Development Factor Method (DFM), Bornheutter Fergusson method (BF), and Initial Expected methods

Machine learning enhanced selections

InsurSight's machine learning powered assumption setting separates the signal from the noise in your data, helping you set assumptions with more confidence.

Dashboards are fully customizable, set up in seconds, and highlight the key features of each loss reserving class.