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Orchestrating Technology Transformation in Commercial Insurance: Part 2

August 14, 2020 | Ashish Lall

In our previous blog, we discussed how broker-underwriter workflow dynamics can be transformed and bolstered with the help of future-ready business-automation platforms.  We take that discussion forward with another example of commercial insurance workflow dynamics – the processor-to-reviewer (P2R) process.

Here, the processor is the business user that receives information from a stakeholder (broker or client) and inputs it into the policy administration system or other insurance enterprise applications. The processor may be extracting the information from a single document and switching between multiple screens to enter in the data in the correct fields, resulting in underwhelming organizational productivity and high data processing costs. Further, another business user – the reviewer, sometimes an underwriter or his assistant – validates the information being entered across various applications to minimize human error and deliver a better end-customer experience. That maker-check approach to insurance operations ultimately increases operational expenditure and puts pressure on profitability.  

Transforming Data Processing

Advanced technologies can foster increased efficiency for commercial insurers to transform the Processor to Reviewer (P2R) process which has traditionally relied on its frontline workforce to process information. With the intervention of AI-based applications and technology tools, such as Optical Character Recognition (OCR) and Natural Language Processing (NLP), the frontline workforce can gradually transition to a role that involves reviewing the data extracted by AI and ML applications. That would free up employee bandwidth, allowing the workforce to focus on more value-adding activities.

Using robotic process automation (RPA) bots, powered by Machine Learning algorithms and AI, insurers can address challenges around productivity, accuracy and turnaround time. From collating large volumes of unstructured data from emails and document management systems to extracting data from submission and claim forms, RPA can deliver significant business outcomes. Such automation levers offer an excellent solution to the difficulties of capturing, validating and streamlining unstructured data in insurance operations. In our experience, we have found that RPA enhanced by artificial intelligence and machine learning can cut data processing costs by up to 50 percent and drastically improve accuracy. 

Automation Challenges

For P2R automation, data standardization is a major hurdle, especially for large, commercial insurers. Data is often extracted from a variety of sources and arrives in multiple formats. So, establishing a set system for this unstructured data to be processed, accessed, updated, and utilized is challenging. The bigger the business, the larger the volume of data, and consequently, the more extensive the standardization exercise.

A good example of large-scale standardization is that of 30,000 ISO Forms across 29 lines of business that provide a carefully constructed framework catering to a wide range of liability and property risks. Similarly, ACORD’s Property and Casualty Standards allow stakeholders to improve their capabilities across all levels of the insurance value chain. The P&C standards are available in two formats, namely AL3 and XML. AL3 is designed as a one-way batch communication process for policy and commission data. On the other hand, XML supports real-time business transactions through request and response messages.

The Way Forward

There are encouraging signs that an incremental revolution is underway. For P2R systems, companies have developed standalone platforms that can extract information from submitted documents using OCR and NLP. Once this information is reviewed and validated by the processing engines, the submission can be screened for relevant sanctions and duplicates. Advanced rule-based engines work behind the scenes to automate these one-stop platforms, where the information is pushed into the systems after the evaluation process to create accounts and submissions through Robotic Process Automation (RPA) or with Application Program Interfaces (API).

Hyper-automation across insurance operations is positioned well to deliver cost efficiency, time savings and significant increases in productivity. However, if insurers are to reap those benefits, preparing a prudent digitalization roadmap and prioritizing automation initiatives is essential. Further, managing the automation journey effectively and collaboratively, through implementation and subsequent fine tuning, will play a critical role in scaling up automation initiatives.

To learn how Xceedance can help you deploy a high-ROI automation solution, write to us at contact@xceedance.com.

Ashish Lall is vice president of corporate strategy at Xceedance.

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