Insurers still struggle with PDFs, images and handwritten documents. Countless human hours are required to manually extract the data into a machine-readable format. This process is known as ETL (extract, transform and load). Insurers that can maximize their ETL capabilities have a powerful competitive advantage.
Enter optical character recognition, also known as text recognition. OCR, which converts text from scanned paper documents, photos, books and PDF files into a machine-readable format, isn’t new. What is new is coupling OCR with AI and machine-learning algorithms to reliably generate text that can be processed, indexed and retrieved.
This technology can leverage existing data sets that comprise thousands of scanned documents and images already interpreted by humans. Smaller, manageable tasks are tackled first. AI and machine learning train and optimize algorithms, and over time the need for human intervention decreases in all departments.
Boosting Marketing and Sales
In employee benefits insurance, AI-enabled OCR can assist sales and underwriting teams by automatically extracting key details from RFPs and lengthy policy documents. It can automatically scrub RFP emails, multiple PDF documents, plan booklets and even scanned images of policy documents for key details. This data can then be loaded into the insurance company’s sales and underwriting systems, such as a quoting and rating engine, to create an initial “shell” quote in seconds.
Insurance companies typically have vast quantities of historical data in unstructured formats. You can call those vast quantities big data--or just an information goldmine!
AI-enhanced OCR can capture relevant data, enabling leaders to identify trends, make predictions and develop products in response to customer preferences.
Speeding Claims
A quick, painless claims experience is key to maintaining customer loyalty and brand reputation. But you can’t sacrifice quality for speed. Insurers must have accurate data for claims processing to avoid overpayments, fraud and legal challenges and penalties.
OCR-enabled claims processing automation software can help speed claims with little or no human intervention without sacrificing quality. For example, clients can take a picture of the receipt from their dentist or car mechanic and send it to their insurer. OCR software can structure the data from the image of the receipt and confirm that the transaction is legitimate and process the claim–all without human intervention.
In 2016, Lemonade famously set a world record for the fastest-processed insurance claim. Their digital assistant, equipped with OCR, received a claim for a stolen $979 coat, checked the claim against the policy, ran 18 anti-fraud algorithms and made the payment in less than three seconds.
See also: Untapped Potential of Artificial Intelligence
Giving Customers a “Wow” Experience
Redundant data entry and drawn-out document-processing times contribute to a poor customer experience. McKinsey found that 30% to 40% of an underwriter’s time is spent on administrative tasks such as re-entering data or manually executing analyses.
Today’s clients demand rapid turnaround for quotes, enrollment and claims. Insurers that fail to respond rapidly will lose clients.
OCR reduces human errors and lets underwriters and other professionals focus on more valuable work. OCR also lets clients process their own documents, which can help insurers speed processes.
Challenges and Best Practices
Like any emerging technology, AI-boosted OCR has limitations. Insurers should look to solutions that emphasize flexibility and configurability. Effective OCR applications can help insurers extract, transform and load text data and present it in any fashion they’d like, whether it’s tables, line items or any other format conducive to business needs.
Ineffective applications can result in losing time and money on technology that doesn’t work well.
Image quality is an important consideration. Thanks to machine learning, OCR applications can interpret more challenging data sources over time. To produce the best results, they should be fed a steady stream of high-quality image data with appropriate lighting, contrast and resolution. Camera applications that contain real-time image quality assessments can help users submit documents for clean processing.
OCR, like all AI-based technologies, requires continuing human inputs and oversight. The more data collected and verified by humans, the faster an OCR application will be trained. Transparency and explainability are always important when dealing with AI, and OCR applications must enable humans to easily understand AI-based outcomes and correct any problems.
See also: 20 Insurance Issues to Watch in 2022
How to Get Going
Insurers need OCR applications that are flexible, straightforward and customizable. But they don’t often have the required internal resources. As a result, many insurers are turning to industry partners with access to AI specialists, large data sets for training algorithms and cost-effective OCR services provided at scale.
To take full advantage of OCR, insurers need modern sales, underwriting, policy processing and claims systems. Legacy systems can sharply limit the effectiveness of OCR and make integration costly.
Getting the most out of this emerging technology takes research and planning. But it’s becoming increasingly important to adopt it to stay competitive.
