How Insurance Industry can overcome the challenges with AI?

Insurance carriers see increasing pressure to process claims quicker and reimburse almost in real-time for common claims. Yet old It systems and slow backoffice are opening the door to nimble startups like Lemonade to take the market by storm. Digital Transformation in Insurers must transverse all customer experience.
The total time to settle claims requires not only effective IT systems but also to quickly process information of documents that support the customers’ claims. This article address how to address this challenge in Insurance company's back office.

Insurance's main essential revenue drivers and profit are customer acquisition and retention. However, knowing how to create experiences to acquire and keep customers happy remains challenging, but the core of the challenge is ‘’changing the status quo for traditional insurers.’’

 
Although Artificial Intelligence (AI) solutions hold a lot of promise in terms of improving consumer acquisition and retention, it's all too tempting to believe that innovative technology will improve customer engagement on its own. The human aspect in the use of AI technology is often overlooked in these discussions: how AI tools can improve consumer service and organizational excellence by reflecting on the experiences of users, procedure, and information. To remain competitive, companies must use AI to reduce complexity and friction in both the consumer interface and the systems that sustain it.

The insurance industry has used artificial intelligence (AI) to minimize complexity in the delivery of records and unstructured information, which is crucial in client journeys from recruiting to onboarding, underwriting, claims, and requests. AI in the forms of OCR, natural language processing, robotic process automation (RPA), and machine learning will provide artificial intelligence, the knowledge of information, procedures, and the people who depend on them to make smarter business decisions.

Insurance firms have mainly used AI to simplify document preparation in tailored consumer transactions, a method known as Optical Character Recognition (OCR), a commonly used basic AI technology. OCR tools are guided data extraction tools for incoming information. However, collecting data from documentation with AI-infused OCR software is just a small part of AI's possible utility and opportunity. Insurers may have content automatically analyzed, categorized, and trained to go through upstream processes thanks to machine learning.

RPA (robotic process automation) was used to simplify insurancers' routine back-office operations and procedures. Now that RPA is more widely used, insurance carriers are recognizing that the greater advantage is to use these automated tasks at the front lines of consumer engagement, where insightful understanding and listening to customer demands pays off in sales and benefits exponentially. The focus of AI investment may quickly shift away from the back office and toward direct targeting of digital staff and AI at eliminating friction from interaction.

Processes and content are critical components of the consumer service and competitiveness of the insurance industry. Offerings like plans, claims, adjudication, and enforcement depend on process reliability and content because insurance is a paper and process-centric business. And as AI systems are incorporated, they are currently applied with the intention of automation in mind, and the human experience is considered as secondary.

Insurance companies connect with their clients through the exchanging of records, messages, and unstructured information, independent of the technology used. As AI and automation become more common in client experiences and engagements, document processing inside such processes are added later, forced to the end of the chain where customers open documents from email and input details into their engagement programs. 
Ensure that the right level of automation and decisions are accessible to the customer-facing process by translating records in real-time into process-ready details.

File handling must be at the frontline of automation, but it is always ignored because it isn't completely defined and mapped as a feature of customer interaction. While both OCR and RPA automate these procedures, they are often implemented in projects in stages to improve internal processes.

In addition, insurance papers are difficult to automate due to their complexity and multiple layouts, and normally come in large quantities. Notifications of failure, surveys, forecasts, invoices, and supporting records are full of free-form text, nested tables, and variable information in unpredictable formats from variable sources; they create a daunting computing task when handled separately, but become exponentially more challenging when handled in large quantities.
Understanding the context, consumer desires, and paper nuances, as well as solving these problems in real time, pose a major opportunity for AI.

It appears that these functions have been digitized now that scanners have been replaced by smartphone cameras, email, and other digital platforms. Although if the information sources are digitized, issues with inputting information from these records into consumer applications remain largely unaddressed.

In the insurance industry, there are two separate tasks that are considered to be the same: method and processing. Customer experience and interaction exist in the flow of activities and actions, and automating these engagement processes is the path to tempo, consistency, and satisfaction. Customer interaction is a mechanism in which data from records must be specifically inputted at different stages.

The method of locating, preparing, identifying, classifying, reading, and extracting, validating, and releasing data into applications, structures, and stakeholders for decision-making is known as processing. Although insurance companies have increasingly digitized document feedback through smartphones and PDF attachments to emails, the actual processing of their content takes place at the end of the process rather than at the point of engagement.

As a result, insurance firms are sacrificing the “now moment” with their clients by deferring the delivery of the most important content provided by their customer's claims, supporting documentation, announcements, audits, and invoices, to the end of the process. Instead of fully automated paper collection, this branch approach creates more friction, delay, and dissatisfaction for consumers, with less good evidence available now for decision-making and follow-up.

Digital intelligence applied during document management will solve these content and workflow issues, but plug-and-play automation and OCR initiatives all too often oversimplify them. Or, even worse, concentrating only on one aspect of the operation rather than the entire. The real advantages of automation in consumer journeys are guided by understanding the meaning, interactions, people, and purpose of the collected data. In both of these fields, AI has made significant progress. Targeting end-to-end paper processing for human-machine communication as an integral part of the consumer experience is where AI can help.

DocDigitizer is an AI Cognitive Data Capture Solution that provides (near) 100% accuracy in data extraction without any validation on the customer’s side.

• Partner Ready – DocDigitizer unlocks amazing opportunities for partners such as integrators, RPAs, ERPs, KYC/AML, and any Digital Transformation scenario.
• Zero Setup – No initial setup, infrastructure, license fees, or troublesome implementation.
• Pay-Per-Document – A flexible cost per document payment structure. No minimum payments required.
• Scale Fast – Start small. Scale up or down instantly.
• (near) 100% Accuracy – Much more accurate than OCR and Data Capture. DocDigitizer curates the extracted data and validates it for you.

Learn more about the difference between DocDigitizer and traditional OCR software by reading about how our system works.