Barnett Waddingham scoops the prestigious risk innovation of the year award for its quantitative analysis tool, validateR, which expedites internal model investigations through an advanced web-based application.

InsuranceERM's judges agreed the risk innovation has improved the insurance market's governance and controls by design, creating a robust framework for delivering analysis of change, model use, benchmarking, and stress and scenario testing.

Barnett Waddingham's primary objectives in developing the validateR tool were to reduce team workload, improve efficiency and boost team morale by eliminating tedious and manual tasks.

In this respect, BW validateR has achieved all these goals and the consultancy cited several case studies to this effect. One senior team leader noted how the solution enabled the team to save approximately 10 person-days per full model run.

In another example, one manager said using validateR had enabled him to reduce the time spent on checking outputs and catching common mistakes.

BW validateR also opens opportunities for innovation by creating a parallel workstream in the modelling team and integrating R or Python programming into general insurance work.

Commenting on whether Barnett Waddingham plans any enhancements to validateR in the next year or two, Wan Heah, a partner and consultant at the firm, says validateR already streamlines a broad range of validation tests for insurer's capital models, enabling more time to be spent on value-add validation.

He adds: "Over the next two years, we plan to incorporate even more tests into the catalogue. Our goal is to add further capability to insurers' validation and capital teams throughout the course of the year, allowing them to move beyond only performing validation as part of their annual capital assessment.

"Our development plans are influenced by feedback from our users to prioritise those tests that they will find most impactful first. In the longer term, we are exploring extending the tool to be able to validate other stochastic-based models."