With new technologies and evolving customer expectations driving rapid change in the insurance sector, research suggests that more than 65% of insurance carriers will adopt at least limited automation by 2024. But, today, the insurance sector largely relies on multiple layers of manual processes that make customers wait while employees try to make sense of complex documents.
Intelligent automation (IA) offers insurance businesses an opportunity to revolutionize the way they operate to meet increasing demands from customers and pressures from the market and to plan for future, unanticipated interruptions. Through the combination of robotic process automation (RPA) and machine learning (ML), IA solves complex enterprise issues through the end-to-end automation of a business process.
The Insurance Ecosystem Involves Many Parties and a Deluge of Data
Many third parties are involved in the end-to-end insurance lifecycle. That’s the case whether you are in commercial, employee benefits, retail or any other type of insurance. A lot of information gets passed around.
Brokers and insurers share data and documents. Advisers working with clients provide information, as do others, such as loss adjusters and lawyers. And the data arrives in the format preferred by the person who shares it.
That Data Comes in a Variety of Formats
Data used for insurance purposes comes in many formats — structured, unstructured and semi-structured — and must be ingested, understood and digitized with accuracy before any automated processing takes place. This involves making sense of data such as cursive handwriting, which is commonly found in life insurance change-of-address and name-change forms, as well as in beneficiary documents. Insurance entities must extract data from highly unstructured employee benefits documents, such as dental, income protection, long- and short-term disability and medical documents.
Brokers and insurers also need to compare and extract data from binders/slips, which can be up to 400 pages long and may use different words to describe the same thing. Insurers looking to ingest unstructured data (like email attachments, handwritten documents, PDFs and unlabeled data) — which is estimated to compose 80% of any enterprise’s data — can find their answer with cognitive machine reading (CMR).
While the industry's standard data ingestion tool — optical character recognition (OCR) — can digitize structured data, it falls down when it comes to reading and extracting unstructured data such as tables, checkboxes and many other forms. In addition, OCR can't read and digitize handwriting and signatures.
A CMR-enabled intelligent automation platform (IAP) can analyze and process large amounts of unstructured data and complex business contracts in a fraction of the time it takes with traditional, manual processes. An IAP enables insurance companies to address the error-, labor- and time-intensive challenges involved with human-driven processes.
For example, a global broking client wanted to extract 17 data points from commercial policies and endorsements. The documents came in from many different insurance carriers and in varying formats. All the data points required rules or reference tables to make the output usable, and most of the data didn’t have labels. In just a three-week period, with the samples of only 220 documents, with 40 different formats, multiple insurers and 10 coverage types, an IAP learned to extract 98.7% of the data, with 96.8% accuracy. Following this proof of concept, the client decided to implement this solution in multiple geographies.
See also: Automation Lets Compassion Scale
CMR Allows Insurance Entities to Do More
John Hancock illustrates the many benefits businesses can derive from a CMR-enabled IAP. The company originally used manual processes to handle the large volume of policy management documents it received. Many of those documents held vast amounts of unstructured data — especially handwritten text in bold and cursive.
Since adopting AntWorks’ CMR-enabled IAP solution, John Hancock has enjoyed higher business productivity, lower turnaround times and a more than 65% increase in accuracy for handwritten cursive recognition. Because the AntWorks IAP uses assistive and adaptive machine learning to learn from exceptions, the system’s accuracy gets better over time.
Insurance entities also can greatly increase their case volumes with the help of CMR. Using manual processes would require armies of people to do validation checks and take a lot more time, while producing higher error rates.
One of the world’s largest human resources consulting firms implemented AntWorks technology to manage large volumes of data and provide optimized quotations to customers for new policies and renewals. This company eliminated manual keying and automated healthcare claims-related processes by extracting data from paper documents and validating for accuracy. That enabled 70% faster processing and a 40% increase in accuracy.
A Fortune 500 insurance company that provides title insurance protection and professional settlement services found that the manual process of validating title documents was leading to error-prone and inconsistent output. CMR technology enabled this company to increase field accuracy across orders by more than 75% and increase productivity by 200%. (Field accuracy is one of the key performance indicators that insurance companies, their technology suppliers and analyst firms like NelsonHall use to evaluate automation solutions. For example, NelsonHall in its SmartLabTest evaluation of document cognition platforms looks at the proportion of fields correctly recognized, accuracy of extraction of recognized fields and proportion of fields overall that are 100% accurate and require no manual intervention.)
IAP Equates to Faster Time to Revenue and Richer User and Employee Experiences
When insurers adopt automation, they dramatically improve the experience for all parties — the broker, the customer and the insurer. They relieve employees from doing what is considered value-added but repetitive work like manual data entry. Automation also eliminates the need for error-prone, stare-and-compare work that’s common in the insurance industry.
That elevates the customer experience because IA allows insurance companies to process requests and respond much more quickly. Digitizing processes also delivers a better experience because customers don’t have to contend with the cumbersome process of filling out and handling paper forms. Meanwhile, IA enables insurance businesses to enrich their data with both structured and unstructured data from other sources and use data analytics and predictive analytics to make their propositions better and more personalized.
IA also can enable businesses in the insurance ecosystem to move faster. That can help them to be more profitable.
Imagine a person is underwriting a life insurance case. If the data that person submits for the case is referred, an insurer would then have to go out to a doctor to get a medical report value. The underwriter would need to assess that report to understand whether it’s an acceptable case and communicate with the customer.
Getting and processing all the data can take weeks, delaying the policy kick-off. But if you can use intelligent automation to understand the data within medical reports, use rules to decide whether to accept or decline and automate the outcome, things happen much faster.
The title insurance protection and professional settlement services insurance company mentioned earlier reduced its processing time by 70% after adopting a CMR-based IAP solution. Meanwhile, the human resources consulting firm noted above increased its operational efficiency by speeding turn-around time, leading to higher customer renewals, an uptick in new customers and increased revenues.
Process Discovery Helps Companies Better Understand the Work They Do
Often, a lack of knowledge and understanding of process flows leads to automation failure. If you’re not quite sure which processes are the most optimal to automate or you’re not clear on all the steps involved in your process (and you don’t have time to do workshops with lots of analysts and business subject matter experts to figure things out), then process discovery is an excellent way of understanding exactly how the process is conducted.
Process discovery enables organizations to identify high-value processes for automation by recording actions that users undertake. If an organization can look at, say, 10 different people doing the same process, it can better understand not only how the process really works but also all the variations in the process, including things like the different process times and different applications accessed. The discovery enables the organization to see the steps involved and create automated processes that use the optimal approaches to those processes. The organizations can then apply what they learned to claims data extraction, fraud detection, mortgage verification and processing, account set-up, policy administration, quotation validation, title verification and a wide variety of other insurance use cases.
In addition to helping companies better understand their processes, process discovery can help in identifying opportunities for automation, expedite digital transformation and unveil previously unknown processes for in-depth process mapping.
See also: Evolving Trends in a Post-Covid-19 World
Intelligent Automation Makes Companies More Resilient
Our new normal puts increased focus on the importance of business resiliency. Manual processes work against that because they often mean that workers need to go to physical business locations to handle paperwork. That creates risk in today’s environment. Intelligent automation frees people and organizations from on-site, paper-based manual processes and instead relies on processes that are better suited to today’s digital, distributed, remote work world. IA also scales, as needed, to adjust to changing circumstances.
The time has come for insurance companies to look at ways to improve their operational processes through technology innovation. IA has the capabilities to help insurance practitioners to do business much faster, more efficiently and with greater security.
