Overcoming Manual Processes, High Seasonality, and Slow Turnaround Time
Achieving $5.6b in market capitalization, the world’s largest provider of specialty insurance for classic vehicles was looking to capitalize and build on their growth.
But siloed legacy systems meant they were grappling with overly manual processes, creating a double-edged sword of high operational costs and slow turnaround times on policyholder transaction requests.
And the seasonal volume fluctuations exaggerated these challenges, causing a significant strain on resources during peak periods. As a result, the insurer sought to break the linearity in operations cost while achieving premium growth.
They knew they needed greater efficiency and better customer experience to keep themselves competitive. To achieve this, they were looking to streamline processes at scale while building better operational resiliency and improving end-customer service standards. How? By embracing digitalization and automation technologies.
Deployment of Intelligent Automation at Scale with Improved Operational Resiliency and Response Times
Sutherland’s partnership with the Insurer is more than four years old. We took a “front-to-back” transformation approach to address customer impact metrics holistically and optimize operations in a multi-phase roll-out.
We started with process simplification, standardization, and de-duplication in transactions lifecycles across various functions. Our human-centric design approach to process re-imagination ensured that we looked at the end-to-end value chain, mapping the journey from the end customer’s perspective.
We then leveraged the solid operational foundation built on standardized operations to drive intelligent automation at scale with Sutherland’s Automation Platform, Robility. Currently, we are implementing multiple automation use cases on 35+ Sutherland-operated, insurer-retained processes.
The operations run in a greenfield setup, which has evolved to support 20+ core insurance processes from our Center of Excellence. We minimized variance in metrics across similar transactions and re-modeled the entire engagement construct to a “per transaction” model, insulating the client from all operational risks while delivering predictability in performance and costs.