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Data Extraction with RPA to Achieve Seamless Insurance Processes

Nov 22, 2022
Robotic Process Automation

RPA Data Extraction

We live in a data-driven epoch in which data is becoming a significant driver of success in any industry. The insurance industry is no exception. In this data-rich world, insurance organizations collect a massive amount of data every day with the goal of risk mitigation, performance optimization, and meeting the ever-evolving client expectations. Therefore, appropriate data processing is critical for remaining ahead of the competitive curve.

In this context, data extraction is a mundane and tedious process that requires utmost precision. For ages, insurers have been facing data extraction challenges, preventing them from utilizing the most that are offered by analytics.

This leads to insurers wondering how they can make the data extraction process seamless. Is there a probability of using RPA with data extraction? Will it help them to yield high accuracy at a reduced turnaround time?

Moreover, the insurance sector is plagued with the inability to meet broker and customer expectations. This is because of high volumes, manual handling, and complex data, leading to a far-from-ideal customer experience. Hence, insurance carriers are also worried about how they can respond more quickly and efficiently to incoming data for submissions and renewals. This is where Robotic Process Automation can become an excellent solution.

RPA is a simple, user-friendly, and cost-efficient solution for data extraction challenges. RPA-based tools can extract and digitize both unstructured and structured data from multiple sources like emails, word documents, spreadsheets, PDFs, etc.

This blog will walk you through the challenges faced by conventional data extraction processes and how RPA can make the process easy & seamless. To begin with, let us first discuss the challenges faced by insurers with traditional data extraction processes.

Data Extraction Challenges Faced by Insurers

  • Multiple Data Sources

Over the years, insurance carriers have created various communication channels. For example, to meet customer expectations, insurance companies have established contact centers, offline and online forms, and, more recently, self-service portals, mobile apps, etc.

While clients are provided with alternate options to suit their needs, a wide range of difficult-to-aggregate data sources is also created, which in turn poses a big challenge for extracting data manually.

Moreover, due to the absence of appropriate data segregation & extraction process, a more or less hybrid approach is adopted in building analytical solutions. This creates data silos that lead to inefficient organizational processes and hinders healthy communication, preventing insurers from gaining the full benefits that data analytics brings and curbing business growth.

  • Inaccuracy

In traditional systems, Data Extraction Tools & Techniques and input are manually done by different data operators. For instance, numerous online claims portals are fed manually into policy systems. This is often done with a lack of attention to detail, leading to inaccuracies like missing descriptions, etc.

Besides, the modern and advanced systems to process data and build models can only succeed if the data extraction & entry process is executed accurately.

  • Lagging Data

In the insurance industry, a considerable amount of unstructured data gets scattered as it comes from several sources. Henceforth, it needs to be appropriately aggregated, cleaned, formatted, synthesized, and shared.

Without an efficient process in place to bring this data to the relevant people, the information becomes outdated as it is very difficult to share. A redundant data infrastructure that is of no use is an additional bane for insurers.

How RPA in Data Extraction Helps Insurers Boost Business Productivity?

Manual data extraction implies that insurers hire several resources for the task, which is mundane and tedious. It takes a lot of time to extract data manually, and there are high chances of errors, thereby increasing the overall operational costs.

This is where Robotic Process Automation comes into the picture to boost the extraction process. The best part is that it helps in saving a lot of resources and time, which can be utilized for value-added activities.

this is how rpa helps in data extraction process

Here’s an example for you to understand better: 

An insurance company receives a lot of emails every day. Segregating and extracting these data and forwarding it to the concerned team manually turns out to be a massive bottleneck for the employees. However, with RPA in place, the bots can extract data rapidly and seamlessly.

Apart from the benefits discussed throughout the blog, let us take a look at some other advantages of automated data extraction:

Increased Efficiency

Automating the manual data extraction process reduces operating costs and delivers efficiency. What may have required ten people to manage the intake process may only take three people with automated data extraction support.

Moreover, extracting the complete set of relevant data from incoming documents simultaneously creates further efficiencies. The information needed for various underwriting support can often be extracted without further manual intervention.

Accuracy & Speed

What if the response time to a submission or renewal request could be decreased by several days? And what if each insurance document had fewer errors? Faster and more accurate quotes increase the likelihood of it converting into a policy. RPA-driven data extraction enhances speed and accuracy, delivering a competitive advantage, leading to business growth and increased customer retention.

Improved Triage

Leveraging RPA in digitizing incoming information unlocks the ability for real-time triage. If a carrier is presently processing only 50% of incoming requests, profitable business is probably left lying in the unopened mailbox. Automated data extraction will enable triage that helps insurers evaluate all submissions to ensure that the underwriters prioritize risks, which is crucial for enhanced profits.


All insurance automation professionals depend on accurate data for valuable insights. Automated data extraction provides a rich set of information that can be analyzed for multiple purposes. Besides, RPA-driven data extraction helps to unlock data from all incoming submissions.

From a carrier standpoint, the appropriate data extracted from all these sources enables to reveal opportunities for pricing, future marketing, and product development.

Intakifi – Intakes Simplified

As a part of our offerings, KMG extends Intakifi, a cloud-hosted SaaS software that is developed to accept all files like PDFs, excel files, and even word documents sent as a part of a submission. It then uses Advanced RPA techniques to validate the files & extract the required data, and transform the data into the format required by your systems. It fits seamlessly into your existing workflow – accelerates the process, and ensures accuracy and round-the-clock service. Please visit to learn more about Intakifi.

To Wrap Up

The bottom line is that for data to support decision-making across all aspects of the automate insurance clams business, from underwriting to marketing and pricing to policy servicing, it has to become viable. In this regard, RPA can facilitate the execution of a seamless data extraction process. This can lead to reduced turnaround time, decreased expenditure, and enhanced accuracy.

Get in touch with us to know how RPA can accelerate your data extraction process and enhance business ROI.

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