In recent years, the insurance industry has seen an influx of new market entrants seeking to disrupt and reinvent specific business segments by launching new products and services free of the expense and overhead of “traditional” players. These digital “InsureTechs” have the advantage of leaner, more agile processes and lower transactional costs. Big incumbents, despite all their scale and financial leverage, often have a tough time competing with them.
Ironically, advances in new digital technologies like Artificial Intelligence, specifically applied to process automation (what some call Intelligent Process Automation), are levelling the playing field when it comes to organizational agility and operating efficiency. And claims processing is one particular area where larger insurers can take real advantage of these opportunities to keep pace.
The Challenges of Automating Claims Processing
Insurers have invested a lot in automation in recent years with great results. But claims processing remains a fairly manual process because of the large amounts of unstructured and semi-structured content that are typically core to the process. For example:
- Most claims processes are full of raw material that can be leveraged to accelerate the cycle time of a claim in the form of the claim data itself. These might include adjuster notes, call center notes and more. The big challenge is that the majority of the information is – primarily text and images. Sometimes they are also tagged with codes and categorizations that define the type of communication. Unfortunately, these are rarely consistent. That makes them difficult to translate for extraction and automation.
- Notes generated by agents during claims processing are also a potent source of information to drive analytics for the claims process. Understanding patterns in claims processing and analyzing them in reference to other structured targets such as loss and volume can present significant value. However, the unstructured nature of these notes makes any detailed analytics almost impossible.
- Some insurance customers submit claims via a document format or web interface. Once received, it may be manually reviewed and classified using a set of claim classification codes. Then the claim is routed to the relevant downstream process based on the code. In some cases, one claim can be assigned multiple classification codes based on the information within the claim submission.
In each of these scenarios, it is very difficult to analyze this content to pull out the required information. In order to process a claim, this data needs to somehow make its way into the company’s Claims Processing system. Today, the “input” still relies heavily on manual data entry or non-scalable search and taxonomy-based approaches. Neither is particularly efficient – creating opportunities for errors and long cycle times.
Ideally, each claim would follow a set of pre-defined workflows; e.g., what Robotic Process Automation (RPA) solutions do. RPA is widely adopted in many insurance companies, but they are not able to handle all the related claims data that comes in unstructured form.
Intelligent Process Automation
Intelligent Process Automation (IPA) is a new approach that is particularly well-suited to the challenges of claims processing. For one, it is specifically designed for all the unstructured content and document-based workflows that are so important to accurate claims processing. Unlike other automation technologies, IPA has the ability to understand text, images, documents and other unstructured data. It can “learn” a set of tasks related to a business process and gives claims processing personnel the ability to dramatically improve their throughput and efficiency.
Intelligent Process Automation complements RPA solutions by providing a way to automatically inspect the unstructured data within claim submission documents and automatically classify and annotate a new claim such that it can be effectively routed to the right SME for evaluation and processing. IPA can handle those workflows that can’t be automated using RPA. It takes unstructured content and plugs it back into business process flows in structured form.
The result is faster processing times and improved accuracy driving improved customer satisfaction and organizational efficiency.
Getting to ROI More Quickly
To take full advantage of Intelligent Process Automation in your claims processing, there are three core things you need to have:
- A clear and shared understanding of the claims processing workflow you are looking to automate or augment. Don’t assume everyone involved in the process shares the same view of how the process works.
- Representative data, content or documents for your claims process. You don’t need a lot of it, but you need good examples.
- Input from the key SMEs to define and evaluate success. Target those people most involved in your claims processing workflow. Who will automation benefit the most?
With these in hand, insurers can automate their claims processing workflow and achieve initial return on investment in just 3-4 months, including development and deployment of the process.
Other Insurance Use Cases for Intelligent Process Automation
There are a number of other use cases in Insurance that can benefit from Intelligent Process Automation (“IPA”). The common thread is anywhere a given set of documents need to be evaluated, reviewed, and/or classified, etc. by a number of different people. For example:
- Appraisals: IPA can process both written and image-based information for property and casualty-related appraisals to verify the assets being covered. Home insurance is the best example here where photos of each room in a house as well as exterior photos can be matched to the written property description.
- Commercial Underwriting: Often involving thousands of pages of documentation, major commercial underwriting processes can be dramatically improved by creating underwriting criteria attributes that can automatically be recognized and “scored” using IPA resulting in major reduction in response times when submitting proposals.
- Policy Analysis: A common challenge in insurance is the need to be able to traverse very large collections of policies that often span several decades to understand how the language within the policies is affected by changes in regulatory policies or the competitive landscape. IPA can understand specific clauses in policies and score and classify them for a given use case.
- Regulatory Compliance: As a highly regulated industry with dozens of state and federal regulatory bodies, responding to regulatory inquiries in a timely manner represents a large expense for most insurance companies. IPA is able to create augmented responses to inquiries dramatically reducing the response times and resources required.
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