Property and Casualty (P&C) endorsement for commercial insurance lines can be a complex process, especially in premium-bearing cases. A great deal of experience is needed, with complex decision-making and risk analysis based on a substantial database of risk codes, location codes, vehicle codes, official regulations, and more.
From the intake process right through to the generation of the endorsement pack, there are multiple handoffs between brokers, agents, underwriters, account analysts, and raters to process the request (whether in structured or unstructured format), to create a manual work request, to toggle through multiple manual tools, and to conduct manual data entry in the systems of record. It’s no surprise, then, that for a client we were able to reduce the current average turnaround time to process an endorsement from 10–15 days to 2–4 days.
Enter smart technologies
This is why insurers are turning to artificial intelligence technologies, including machine learning (ML) and natural language processing (NLP), to take over these repetitive, demanding and burdensome tasks.
For example, a global professional services company has automated the analysis and classification of incoming text by applying machine learning for administration and servicing processes. Documents can be analyzed and classified using NLP and ML algorithms. This tool is also trained with historical data that enables it to classify, understand, and extract the required information. It also links customers’ policy documents to business processes, prompting different functions to take actions, where API triggers a process chain, robot, or agent, so that the necessary processing can be executed.
Robotic process automation (RPA) bots are also being deployed, for parsing data, such as name changes, phone number changes, mortgage updates, policy cancellations, risk improvements, discount requests, coverage exclusion updates, coverage addition requests, and the inclusion of additional parties. The data is then used to populate or update the policy accurately.
One home insurance company that used these techniques to automate its policy endorsements achieved a 30% reduction in manual effort, higher employee satisfaction, a 20% reduction in process costs, and improved customer experience. The insurer also achieved 90% consistency for in-scope business processes, with only 10% being transferred as exceptions.
Similarly, a global insurer that was looking to automate manually dependent back-office processes within policy operations of its workers’ compensation took advantage of a deep-dive RPA assessment that helped in identification of 100 automation opportunities. This resulted in $7 million in net savings, and 80 full-time employees being redeployed from manual back-office tasks.
Digitization in policy operations is rapidly evolving, with some insurers and vendors preferring single comprehensive platforms, while others pursue a hybrid approach. Either way, the momentum behind the adoption of automation is growing, and smart technologies are expected to change the face of the industry over the next decade.
Automating routine endorsement tasks will enable smarter and faster processing, resulting in the achievement of industry-leading performance metrics for insurers with a focus on efficiency improvements, top-line growth, lower operating costs, accelerating speed to market, and most importantly, a radical and beneficial transformation of customer experience.
The benefits of introducing smart technologies to the P&C endorsement process are many:
- Accelerated turnaround times and improved customer and agent satisfaction through process efficiencies
- Reduced billing issues downstream and customer service/status calls
- Employees are able to pursue higher value-added activities, leading to improved satisfaction and morale
- Higher and faster returns on investment can be achieved.
Kanhiya Singh is a Senior Solution Consultant with Capgemini’s Digital Insurance Operations (DIO) practice. He designs innovative digital BPO solutions and offers for global P&C, life, and health insurance carriers.