Read on for four key ways that insurance companies are using OCR to drive financial performance and create even better customer experiences.
Before the advent of OCR, insurance companies scanned paper documents and turned everything into flattened digital image files. They then performed a manual indexing operation on each document, which required a person to re-key data read off an image on one screen into a file on another screen, and then decide where that data should be filed. Because of the human involvement in manual indexing, this method is error-prone, inconsistent, onerous, and expensive.
And, in many cases, manual indexing is impossible to implement. Take, for instance, a large insurance company that receives more than 20 million documents a day. They can’t hire enough staff to process the data manually—even if they could, re-keying that volume of content would create considerable delays in the downstream workflow.
In the insurance industry, delays could tarnish the customer experience. People reach out to their insurance company when they’re dealing with a crisis—whether it’s a fire, theft, or a medical issue that requires immediate resolution. Delayed payments create added stress and erode good faith in an organization’s customer service. Over time, consistent delays can result in deteriorating client satisfaction and retention rates.
One insurance company solved the volume-versus-customer-service conundrum in an unconventional way: by taking a burdensome manual process and offloading it to their customers. They created a portal and app that enabled customers to scan or upload their documents. After the content was uploaded, it was put through an OCR process to return a fully text-searchable PDF document that was ready for automated sorting and analysis.
By outsourcing document onboarding to their customers, the company increased the value of the data capture process exponentially. The organization couldn’t hire enough staff to deal with the volume of incoming content, but they realized they had an ace in the hole: they had far more customers than employees. In effect, the clients became the company’s largest workforce, able to process their own documents immediately in a format ready for downstream use.
This new approach didn’t just save time and money; it also increased customer satisfaction. Customers had control over their own data, and they took ownership of reviewing their submissions for accuracy and making any necessary corrections. Costs, errors, and processing time were all reduced, while service and satisfaction were increased.
One of the key ways OCR helps insurance companies attract more clients is by enabling an organization to do a better job than their competitors when it comes to matching their products and services to customer needs. Some organizations, for instance, now have online solutions that allow potential customers to upload a copy of their current insurance policy.
This allows automated analytics tools to analyze the policy, and then compare it to the company’s own products to see if they can offer the client better or cheaper protection. No manual process is capable of handling that front-end data capture efficiently. It takes a good OCR process to automatically transform client documents into accurate data that’s in a format fit for automated analysis.
Insurance companies are always looking for ways to improve margins and profitability without sacrificing customer service. OCR enables enterprises to lower their costs by eliminating manual steps in their data capture methods and reducing errors and omissions.
One office, for example, had 35 employees manually reviewing and re-keying incoming documents on a full-time basis. This process was very time-consuming and expensive. After implementing an automated OCR process, however, they were able to reduce that manual workforce to just two people at the front-end of their data capture process. And those two employees didn’t do any manual indexing—they only dealt with exceptions and errors.
The switch from manual indexing to an automated OCR process by itself would have resulted in a reduction in the resources required to onboard data. Additionally, it would have reduced errors and omissions introduced by manual data entry from five percent to as little as two.
But, a small number of errors result in the bulk of the costs in a data capture process, mostly in the form of time spent searching for documents that had been incorrectly indexed manually. By keeping those two employees involved at the front end of the system, the small percentage of errors were identified and corrected. As a result, all documents were correctly filed and easily found when needed in the downstream process—saving the office as much as $1.25 million a year in search costs.
OCR has greater benefits to insurance companies than just improving the data capture process. It can also be applied to the vast volumes of legacy data—much of it unstructured paper and image files—companies have accumulated over their lifespan. OCR can convert these warehouses of data into formats that are usable by Artificial Intelligence (AI) or Machine Learning (ML) software like IBM’s Watson or Google’s AI libraries. These tools can then sift that historical data (as well as the flood of incoming new data) for clues pointing to trends in the marketplace, future changes in customer behavior, and emerging risks.
Some insurance companies, for example, use OCR along with an AI solution to identify health-related changes in customer behavior over time. The companies feed OCR-processed historical client data into AI solution and combine that with OCR-processed data from external sources—such as census information, clinical trial results, medical studies, or lifestyle questionnaire results. This information can be used to identify trends that result in lower payouts or fewer claims, such as lower disease and mortality rates. In response to these predictions, an insurance company might change the cost-versus-risk assumptions in affected products, or create entirely new products altogether.
One re-insurance company, for instance, processes three trillion pages of data annually through OCR. All that content is fed into Watson, which generates predictions on cause and effect and identifies trends in massive data sets over long periods of time. By applying a low-error, automated OCR process to the content, the company can extract maximum value from its data and harness the true power of AI.
OCR is the cornerstone of data capture and management processes in the insurance industry. It enables companies to get full value from their data and their automated analytics tools. In particular, OCR enables companies to provide better customer service, attract more clients, reduce costs, and even predict the future. Without OCR, digital transformation—and all the benefits it brings—wouldn’t be possible.