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What is insurance OCR?

Insurance sector employees across various departments like marketing, underwriting, and claims face the typical issue of pulling the correct data from diverse sources and altering it into a structure they can examine and analyze.

The mechanical or electrical processing of digitized or photographed reproductions of written, typewritten, or printed text into machine-encoded or computer-readable text/images are known as optical character recognition (OCR).

This technology's major applicability is in businesses where there is a variety of data entering and a lot of printed documents to decode. This allows for data retrieval, digital modification, convenient storage, and online presentation by digitizing printed texts. OCR is used in machine translation, text-to-speech, and text mining, among other applications.

This is similar to ETL (extract, transform, and load). Insurers that can enhance their ETL facilities have a stronger competitive edge. Unstructured data sources, like PDFs and images, pose a special problem for insurers since countless human hours are required to manually extract the data into a machine-readable format for processing. Most of these tasks are repetitive and manual processes, which are both high-cost and laborious. As a result, insurers are looking for rapid and more cost-effective solutions like optical character recognition (OCR).

Repetitive and laborious procedures, knowledge/data/organizational silos, complicated business models, and other impediments exist in the insurance industry. In data-intensive sectors like insurance, the main challenge is attaining digital operation efficiency and digital customer experience. Insurance proposal and claim data entry, payroll audit, insurance certificate creation, and invoice processing are just a few examples of sectors where automation has the potential.

Around 30 to 40 percent of underwriting’s work time is expended on administrative chores, such as re-entering data or physically conducting analyses.

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How does insurance OCR work?

In recent decades, technology has transformed insurance in many ways, yet some antiquated aspects of the sector still exist. The industry's reliance on paperwork is a good example of this. Paperwork is not only costly to generate but also time-consuming to manage, expensive to retain and secure, and has a large carbon footprint. Many insurers are aware of this but are unsure how to address it. One solution is to digitize data from documents using specialized optical character recognition (OCR) software.

Before the introduction of OCR technology, insurance providers scanned paper documentation and converted them to compressed digital image files. They then ran manual categorization on the files, which required a person to constantly glance at the screen and decide what data should be stored. This approach is subject to mistakes and unreliable due to human participation. Many firms may have large volumes of files to process and analyze, which could take typical human days or weeks to complete, delaying the entire workflow.

When faced with a catastrophe, whether it's a fire, theft, or a medical issue that demands rapid attention, people contact their insurance carrier. Delayed payments add to the stress and diminish trust in a company's customer service.

Computerized analytics tools to assess the plan and compare it to the company's other offerings to see if they can provide better or less expensive protection to the client. No human technique can effectively handle that front-end data capture. To automatically translate client papers into correct data in a format suitable for automated analysis, a good OCR technique is required.

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What are insurance OCR use cases?

Facilitating the client experience

Providing a seamless, efficient customer experience is an important component of acquiring and retaining consumers. OCR can help you do this by streamlining the digitalization of information for more fluid customer satisfaction and simplifying and speeding up various tasks linked with customers that involve paperwork.

Insurers can provide a more efficient, competitive, and customer-friendly environment by employing OCR to optimize essential operations.

Increasing accuracy rates

The presence of errors is a virtually unavoidable concern with many of these forms of data digitalization. Errors are unpleasant in any setting, but they're also particularly damaging to insurers. If a client is given incorrect pricing, for example, the customer relationship may be ruined, and any earnings may be lost.

Unfortunately, because human error is inevitable, manual data transfer will nearly always result in quite high mistake rates. Furthermore, simplistic variants of OCR are more likely to yield mistake rates that are greater than ideal. However, AI-based OCR is often generally more reliable, rendering it a critical component of any insurer's efforts to improve accuracy rates.

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Onboarding clients

Client onboarding is a vital component of managing an insurance company, and it must be done correctly. In today's fast-moving market, the pace and consistency with which you onboard consumers are critical. Manual data entry and basic OCR, on the other hand, can be slow and error-prone, putting them something a little less than an ideal onboarding alternative.

As a result, AI-powered OCR can be a great method to improve the onboarding process. It can help your company collect and process critical customer information more quickly, conserving your effort and allowing you to get clients up and running sooner.

Automating administration

Because OCR is an intelligent automation tool, it's a natural fit for any company looking to automate some of its administrative procedures. You can save time, labor, and resources by eliminating the need for undue human interaction in basic data handling procedures. These resources can then be reallocated to help the company, and you'll enjoy smoother overall processes and administrative efficiency as a result.

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Enhancing data organization and protection

Shifting away from paper-based storage to digital storage has numerous advantages, including being easier to organize and secure. As a result, insurers can employ AI-powered OCR to help their data organization and security initiatives by speeding up accurate data digitalization.

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What are the benefits of insurance OCR?

Optical character recognition (OCR) technology is important to the insurance sector's growing digitalization. It automates document processing, filtering, and evaluation, making all company operations more productive and efficient, from insurance claim processing to product development to customer relations.

Enhancing customer service

Insurance companies used to scan paper documents and convert them to compressed digital image files before OCR. They then manually indexed every document that needed a worker to re-key data received from an image on one device into a file on another device and then selects where that data should be filed. Manual indexing is error-prone, inconsistent, time-consuming, and costly due to human participation.

Manual indexing is also difficult to implement in many circumstances. Consider a major insurance firm that receives over 20 million documents per day. They can't afford to pay enough people to manually handle the data, and even if they did, re-keying that much content would cause significant bottlenecks in the downstream workflow.

Delays in the insurance sector may harm the customer experience. When faced with a catastrophe, whether there's a fire, crime, or a medical problem that demands rapid attention, people contact their insurance carrier. Delayed payments add to the stress and diminish trust in a company's customer service. Consistent delays might lead to lower customer satisfaction and retention rates over time.

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Attracting more clients

One of the most important ways OCR assists insurance firms in attracting more customers is by allowing them to outperform their competition when it comes to aligning their products or services to customer wants. For example, several businesses now offer online services that allow potential clients to scan a copy of their existing insurance policy.

This allows automatic analytics tools to assess the policy and compare it to the company's existing products to see whether they can provide cheaper and more efficient protection to the client. No human technique can effectively handle that front-end data capture. To automatically translate client papers into correct data in a format suitable for automated analysis, a good OCR technique is required.

Optimizing costs

Insurance firms are constantly seeking methods to increase profit margins and customer service without losing quality. By eliminating manual processes in data-capturing techniques and minimizing discrepancies, OCR allows businesses to save money.

Since OCR in insurance reduces the time, labor, and amount spent correcting errors, it saves time, money, and resources. In the data-gathering process, even a minor inaccuracy might result in considerable expenses. As a result, OCR insurance documentation digitizing eliminates all of these expenses while increasing your profit margin.

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Making precise predictions

Insurance firms benefit from OCR in more ways than merely enhancing data gathering. It can also be used to manage the massive amounts of legacy data that businesses have amassed over time, most of it unorganized print and image files.

OCR can turn large data warehouses into formats that Artificial Intelligence (AI) or Machine Learning (ML) tools, such as IBM's Watson, Power Automate or Google's AI libraries, can understand. These systems can then filter through that past data (together with the deluge of new data) for hints about market trends, potential changes in customer behavior, and emerging threats.

Improving data gathering

Insurance firms benefit from OCR in more ways than merely enhancing data gathering. It can also be used to manage large amounts of legacy data, such as disorganized paper and image files, that businesses have acquired over time.

In the insurance industry, OCR is the backbone of data acquisition and administration. It enables businesses to get the most out of their data and automated analytics tools. Companies can use OCR to satisfy customers, attract more customers, reduce spending, and even estimate the outcome.

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Boosting safety and security

OCR software can assist businesses in taking the first steps toward digital document security. In the insurance industry, OCR can help reduce the prospect of losing records of policies. It also enables insurers to efficiently deal with bogus claims. In this method, insurers can successfully limit fraud-related losses.

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What are the challenges to implementing insurance OCR?

Although the potential of OCR is evident, it, like any developing technology, has significant limits that insurers considering its use must be aware of.

The flexibility of the solutions

Solutions that stress flexibility and reconfigurability should be considered by insurers. Effective OCR software may assist insurers in extracting, transforming, and loading text data and presenting it in whatever format they need, including charts, line items, and any other format that suits their needs.

Image Quality

Another factor to consider when utilizing OCR is image quality. OCR solutions are educated to understand more demanding data sources over time, thanks to machine learning. To get the greatest results, they need to be fed a constant stream of high-quality image data with the right lighting, contrast, and resolution. Real-time picture quality assessments in camera applications can assist the user in submitting documents for clean processing.

Human Supervision

OCR, like all AI-based technology, necessitates continual human input and supervision. The quicker an OCR program is trained, the more data is collected and reviewed by people. When dealing with AI, clarity and interpretability are critical, and OCR solutions must allow humans to easily comprehend and question AI-based decisions.

Easy DIY Solutions

Insurers want OCR solutions that are adaptable, simple, and configurable, yet they frequently lack the necessary resources internally. As a response, several insurers are moving to industry partners who have access to AI experts, vast data sets for testing models, and scalable OCR services.

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What is the future of insurance for companies?

In the last few decades, technology has advanced at a breakneck pace, bringing with it a slew of creative approaches that simplify and optimize the workplace. When it comes to the insurance industry, Optical Character Recognition (OCR) Solutions enable simplified AI claims processing. Any business's customer experience is recognized to be impacted by insurance claims. Payment interruptions can easily happen due to a bottleneck of claims, misplaced forms, or even inaccurate information punched in by a claim specialist. In an atmosphere where insurance claims handling is totally manual, such concerns are extremely common.

Previously, OCR technology was only available through scanning machines or specific corporate insurance business software.

With improved accessibility, the insurance industry's focus is shifting to establishing the next feasible OCR Proof-of-Concept. When OCR is combined with the democratization of AI and the dropping costs of quicker cloud-based computing and storage capabilities, insurance agents and brokers may develop sophisticated document comparison skills to swiftly examine policy papers and react to client inquiries. Claim auditors might scan claim forms and supporting documentation quickly to extract critical information needed to make a claim decision. On complex insurance agreements, consumers may be able to compare pricing, policy descriptions, and sub-limits.

Operators that can effectively extract, transform, and load (ETL) updates can generate more revenue and improve customer satisfaction.

The ETL process relies heavily on optical character recognition (OCR). It enables insurers to convert unstructured text-based data sources (such as scanned documents) into a machine-readable, organized format for backend processing.

Insurers can utilize optical character recognition (OCR) to help their customer service staff alter the client admission and data input process. This can help reduce time and money and eliminate errors while also improving the client experience.

Source: McKinsey

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Nanonets - The Best Insurance OCR Software

Nanonets Insurance OCR software
Nanonets Insurance OCR software

Nanonets is an AI-based intelligent OCR software that can eliminate manual data entry by automating all manual and paper-based processes with in-built OCR software, automated workflows, and 5000+ integrations.

Nanonets is an intelligent automation platform that can be used to extract data from insurance documents like KYC, ID cards, passports, customer onboarding forms, claims, receipts, bills, and more. As an insurance company, you can automate all processes like accounts payable, accounting, customer onboarding, KYC, customer id verification, invoice processing, workflow management, document management, and more with Nanonets.

Here are some reasons why you should consider Nanonets as your next Insurance OCR software:

  • Easy to setup and use OCR software
  • Modern User interface
  • Pre-trained OCR models for invoices, bills, AP automation and more
  • 95% OCR accuracy
  • Supports 120+ languages
  • No hidden pricing
  • 24x7 customer support
  • Pay as you go plans
  • 5000+ integrations with API and Zapier

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How to select the best insurance OCR Software?

Best fit for the business

Various sorts of records exist, banking records, insurance records, law papers, invoice copies, and more. Good OCR software needs to be efficient for the documentation you process in your industry.

Real-time document fraud detection ‍

Whether it’s a scanned image, the image on an image, a PDF, or a photoshopped copy, the software should be able to detect ways of fraud throughout diverse documents in real time and make sure that the data is extracted from authentic references.

Uncomplicated to set up

It should be uncomplicated for the software to be put in place. It shouldn’t make you dedicated to thorough pricing models and try to exploit you in the process. You should be able to extract as many documents as you want, process them, and the platform should adapt to make the necessary accommodations as per your needs.

Document verification

You should be able to emanate insights from data. The platform should be able to organize articles and display varied metrics that businesses can take benefit of for examining extracted data. A good OCR software has to be efficient in processing all the documents you need. The three top metrics to assess the performance of OCR software are character recognition, field recognition, and document automation rate.

To manage the flow of documents analyzed, you need a user-friendly interface to ensure that the computerized extraction of data is accurate. It has to allow a feedback system with people concentrating on complicated situations to accept or reject the automated analysis. It should also have monitoring features.

Easier customer onboarding

It should offer new users a demo to aid organizations in onboarding within a small amount of time. The interface should be very intuitive and novice-friendly.

Accommodating customer support

The software customer support team should be very dependable and functional to get a quick response when stuck in a problem. To reach a satisfactory automation momentum, continued refinement is key. It will enable the software to fit your needs and adapt to your context.


In the insurance sector, OCR is the backbone of data acquisition and administration. It enables businesses to get the most out of their data and automated software solutions. OCR, for example, helps businesses to improve service to the customers, bring new consumers, cut costs, and even forecast the future. Digitalization in all benefits it brings—would not be feasible without OCR.

Insurance firms may drastically enhance production rates while lowering process costs by utilising OCR technology. It allows content from bills, documents, or identity cards to be recognized and turned into accessible and identifiable digital files. Choosing the proper solution is critical to avoid issues such as data processing errors farther downstream the value chain or low extraction accuracy rates.

Nanonets online OCR & OCR API have many interesting use cases that could optimize your business performance, save costs and boost growth. Find out how Nanonets' use cases can apply to your product.

January 2024 Update: The blog was originally published in May 2022 and has been updated in January 2024.

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