Many aspects of the life insurance business require providers to process documents of various types, most of them unstructured. Processes including underwriting, servicing, claims processing, and regulatory compliance all require humans to read myriad documents and extract relevant information for input into downstream processing systems. Such processes are both time-consuming and prone to error, making them ripe for automation.
Providers initially turned to robotic process automation (RPA) for their life insurance policy processing, as well as other automation approaches involving templates and optical character recognition. But they quickly found that such methods only got them so far – because they can’t effectively deal with unstructured data.
What’s required is an intelligent automation solution with cognitive capabilities, based on artificial intelligence technologies, that can “read” unstructured documents just as a human does. Such a solution brings automation to life insurance processes, freeing up employee time and accelerating processes while reducing errors.
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 data 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 data 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 data.
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 firms would need to 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 providers 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%.
If that sounds different from other life insurance AI solutions you’ve encountered, that’s because it is. While Indico’s IPA solution is certainly sophisticated in its use of cognitive technologies including machine learning and natural language processing, we keep the technology behind the scenes, enabling an army of citizen data scientists to use the technology to solve real business problems.
Natural language processing (NLP), for example, is core to our IPA platform. It’s what enables our generalized model to understand the context around unstructured data, just as a human would. But it’s built into our models and functions behind the scenes; there’s no need for those who use the platform to even know what NLP is.
The same goes for machine learning (ML). While our engineering team built our IPA platform using cutting edge ML models, they all sit in the background – there’s no need for users to tweak or otherwise interact with them, or even understand how they work. Citizen data scientists instead can just think about how to apply IPA to take repetition and complexity out of their processes and deliver real business benefits.
Automated 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. Using artificial intelligence for life insurance underwriting 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.
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.
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 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.
Extract relevant data from insurance documents and feed it to an analytics engine to identify any areas where you may be running afoul of regulatory requirements. Indico Data can also help index data to simplify and speed responses to regulatory bodies.
4x increase in process capacity
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.
85% faster cycle time
Automation decreases process cycle times by as much as 85% while improving accuracy, which leads to improved customer satisfaction and reduced costs.
8o% increase in efficiency
Automating life insurance processes frees up time for employees to focus on more rewarding and strategic work, increasing both employee satisfaction and company competitiveness.
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.
Improve application integration
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.