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Until recently, most life insurance companies have been using RPA and other rule- or template-based approaches to document process automation. But these technologies work only for a small subset of life insurance processes – because they don’t work well with the unstructured content that makes up 80% or more of all life insurance documents.
The life insurance underwriting process is one example. It involves collecting various data and documents regarding the health of the applicant. Much of it will be included in the detailed questionnaire applicants are asked to fill out, but may also include medical records, paramedical exam results, and various test results, such as X-rays and EKGs.
Historically, data entry teams read each document and input relevant data into a downstream life insurance processing system. Data entry, of course, is a time-consuming, labor-intensive, monotonous job, not to mention prone to error – making it a candidate for automation.
But given the information to be collected varies for each applicant, and that it comes in different formats from multiple sources, it’s all but impossible to come up with a single template that covers all possible variations. Similarly, it would be impractical, and exceedingly expensive, to try to come up with templates for each type of document that may be involved in the process.
What’s required to deal with unstructured content is an automation approach that has cognitive capabilities that give it the ability to “read” any kind of document much like a human would. The underpinning of these capabilities is a vast collection of labeled data points, which serve to give the tool context behind most any kind of unstructured content.
Indico’s Intelligent Process Automation (IPA) platform, for example, sits atop a database of some 500 million labeled data points. Even the largest life insurance company would spend years, and large sums of money, to collect and label that much data.
Artificial intelligence (AI) technology known as transfer learning then enables life insurance companies to take that vast database and build their own models to address specific requirements – without having to know anything about AI.
Instead, the business people on the front lines, who know the processes best, use intuitive Indico tools to label maybe 200 documents, indicating which pieces of data they want to extract. That’s enough to train an automation model to work with an accuracy rate of about 95%.
Automating document processing in life insurance underwriting is a prime use case, given the many and varied types of documents involved. To assess the risk of a potential client, life insurance companies must collect data on health risks, including potentially numerous documents from myriad healthcare providers. Insurers also need to assess an applicant’s net worth and creditworthiness, which means examining various financial documents. Collecting all this information and pulling out appropriate data is time-consuming and prone to errors. Automating insurance underwriting with an IPA platform can dramatically speed up functions including document assessment and data extraction, assessment of loss runs, and review of the customer’s claim history – all important factors in the underwriting decision.
Processing life insurance claims likewise involves collecting and assessing numerous unstructured documents, including a claim form, death certificate, original policy document and medical reports. Intelligent process automation can help insurance companies implement an automated claims processing workflow that includes extracting pertinent data from all documents, assessing whether the applicant is indeed a qualified beneficiary and if the policy is still in force and active.
Life insurance companies are highly regulated and subject to a constantly changing regulatory landscape. Yet failure to comply may mean fines as well as operational and reputational damage. Intelligent automation in life insurance can help companies stay in compliance by automating many routine tasks, dramatically reducing the possibility of human error that could put compliance at risk. Automated processes also leave behind a log of all actions, which will prove valuable should the company be subject to an external audit. Automation can likewise help with generating regulatory reports and compliance checking processes.
Life insurance policies can change over time, when policy holders move or request limit increases, for example. Intelligent process automation can automate many routine insurance policy management tasks, such as with models that pull change of address requests from emails and transcripts of voice calls – and kick off scripts that complete the request. Intelligent models can also automate tasks such as processing of loss run reports, analyzing statement of value reports, and more.
Intelligent automation in life insurance enables underwriters, adjusters, investigators and others to be more productive, getting more work done in the same amount of time.
Automating life insurance processes frees up time for employees to focus on more rewarding and strategic work, increasing both employee satisfaction and company competitiveness.
Automation decreases process cycle times by as much as 85% while improving accuracy, which leads to improved customer satisfaction and reduced costs.
Automating life insurance processes requires clearly defining how the process works, an exercise many companies never previously undertook. Codifying how each process works means that knowledge is now captured in perpetuity, rather than simply resting with certain employees.
Intelligent automation of life insurance processes often includes integrations with various important applications, including business process management, enterprise resource planning and customer relationship management tools, thus streamlining operations.
Improving accuracy in life insurance processes helps ensure companies stay in compliance with industry regulations, while leaving a valuable audit trail.
Process automation ultimately makes life insurance companies more competitive, whether the competition is an industry stalwart or an insuretech startup.