Claims process automation: An approach to overcome the current challenges

Conventional claims processing can be laborious and taxing as it’s still human-led, and these clerical tasks are repetitive and mundane and impact operational efficiency. These tedious tasks can be costly to many brands; hence, finding alternative and innovative methods to handle claims is in their best interest. This is where claims process automation comes into play.

With its intuitive approach and offering numerous automated tools to fast-track multiple data sets, voluminous information may be consolidated quickly and effectively. With claims process automation, companies can now move to a faster, intelligence-based system that deploys RPA (Robotic Process Automation) to analyze data accurately. According to McKinsey, over half of the insurance claims will be processed using automation by 2030. Moreover, almost 60% had already switched to AI-based automation solutions (including for claims processing) nearly five years ago.

This yields streamlined customers demand information that fulfills responsive processing turnaround times. In addition, it enables businesses to focus more on customer satisfaction rather than quicker processing times. Hence, automated claims processing acts as a ‘digital acceleration tool,’ which enhances the overall customer experience journey delivering faster claims processing with more favorable outcomes for insurance firms and their respective premium holder customers.

What is claims processing?

Claims processing is an insurance company’s process to verify the claim requests filed by an insured for adequate information, authentication, and justification. It is the fulfillment by the insurance carrier of its agreement to obtain, examine and respond to a filed claim.

Additionally, claims processing encompasses the various stages in validating an insurance claim. This ranges from seeking and ensuring sufficient details or information to substantiating any such claim. It leads to possible outcomes that include acceptance, rejection, or settlement of an application based on specific stipulated criteria.

Challenges in claims processing

The first and foremost concern is the steep operating costs. Any delay or disruption can add to the overhead costs due to claims leakage, overlooked opportunities, and occasional wrong payments arising from fraudulent activity and human clerical error. Additionally, service delivery aberrations cause processing gaps owing to bottlenecks such as inefficient management of silos data and skill gaps. Also, poor prioritization and incoherent pathways, along with manual data inputs, all yield errors and inconsistent results.

Data silos can stagnate information flow and ‘liquidate’ them, and companies must proactively share data, ideally over a universal system in a standard, understandable format. Also, skills gaps within the workforce can be a hindering factor, as an automated solution requires a certain level of human involvement. In that case, upskilling the employees is the only way out. Thus, more people can operate or be involved in the skills-based operations required to run businesses productively.

Moreover, fraudulent activities lead to misinformation which wastes time searching through consulting and predictive indicators. This dampens the entire claims processing workflow. This further leads to blind processing in which stakeholders are often oblivious to current trends, so pertinent data to forecast trends is often omitted. Therefore, operating in such vague environments becomes risky, posing many challenges, including outdated information, leading to inconsistencies.

Moreover, inaccurate integration due to sub-standard data practices can generate outdated details, erratic payments, and even worse customer service besides the regulatory compliance requirements leading to unproductive businesses.

How does automation offer solutions to claims processing problems?

Firstly, data capture, copying, and intersystem entry are all essential tasks that, if automated, will salvage time and resources, thereby driving efficiency. RPA powered by AI and ML can then collate information, reconcile data, and verify claims. Implementing details by extracting pertinent values from emails and then entering them into the system is another method of how automation streamlines the claim process. According to a study by Gartner, RPA is expected to undergo double-digit growth through 2024.

Workflow automation with rule-based, algorithmic logic and NLP (Natural Language Processing)-powered cognitive reasoning can also be readily automated for quicker outputs. Graphical analysis and OCR-based PDF document scanning can also benefit from automation.

Automating tasks is as simple as identifying an automatable process, then finding ways to automate it, followed by processing that information into a meaningful report and then analyzing it accordingly. However, given the intricacies of certain sensitive domains, this may be easier said than done.

Nevertheless, the sheer significance of doing so, especially reaping the long-term advantages of replicability and efficiency, overshadows such disadvantages. This makes automatable tasks a desirable avenue to process data quickly, effectively, accurately, and, most importantly, accountably. But, unfortunately, culminating all these factors, automation cannot do anything specific.

Benefits of claims processing automation

Faster processing times: Implementation of automation in claims processing drives efficiency and achieves an excellent synergy between qualitative and quantitative output for ultimate productivity. With automated pathways, accurate data collation, and integration, better product details with a collaborative approach. Simultaneous operational ways are made possible with RPA to switch quickly between various systems.

Enhances data accuracy: With insurance claims processing automation, data accuracy rates are improved and uplifted by reliable and systematic protocol. This enables automatic data capture from a compliant regulatory standpoint via scanning, followed by pertinent inputs into the respective fields. This streamlines the entire claims process.

Reduces operational expenditure: Automation minimizes the company operation costs, whereby savings can be reinvested into enterprise expansion for sustainable development. Employee’s efforts can therefore be refocused elsewhere, especially where it counts or matters the most.

Boosts employees’ satisfaction: Increased employee satisfaction rates reduce attrition and promote retention for a better overall scope and workforce outlook. This encourages workers to perform their best – always. Consequently, claims are processed efficiently and responsibly.

With organized and structured processing with automation, data cleansing, and mining can be managed effectively. The extraction process and personalization generate a stable platform for AI-based solutions to be effectively utilized.

Strategies for executing claims processing automation

ML and AI-based automation power claims to process with their deep learning cycles and intuitive nature. IDP (Intelligent Document Processing) augments the entire process of automating such frameworks with its innovative yet simplistic approach. Better outcomes are ensured by extracting relevant data and details. For instance, First Notice of Loss (FNOL) schematics are now electronically and digitally captured, then processed accordingly. A chat management system or a bot interface interacts with the claimant to acquire further information. Cognitive analytics, data-driven intelligence, telematics, and IoT (Internet of Things) all automate claims processing alongside sensory detection and inputs.

Precisely, for IoT, perception, followed by connectivity layers transmitting data from respective devices to the cloud, facilitates the entire process. This is substantiated by a processing layer that handles streaming data, ending with an application platform to report results and control devices. Moreover, computer vision implements a damage control initiative to combat unscrupulous claims. Databases can be aligned with auto adjudication of claims to ensure that rule-based logic governs any decisions made. Auto information extraction, fraud detection, ML algorithms, and data science principles help run these processes.

Examples include digitizing healthcare records to determine medical insurance premiums more accurately then. First, less or even no form filling means that clerical errors are almost negligible, if not completely eradicated. Secondly, verifying the identity of the claimants to curb fraud and ensure correct party payouts. Thirdly, managing online processes across the customer journey provides an end-to-end breezy experience. Finally, live status updates drive better accuracy rates and can even boost profitability via ROI. Now, the processes of logging, validation, adjudication, and payment can all be streamlined for the betterment of the business.

Insurance companies are under immense pressure to provide enhanced customer services at diminished costs, especially for claims management. However, the future of claims processing management looks bright as customers embrace digital technologies for faster outputs, and insurers are incorporating solutions such as RPA and Artificial Intelligence within their operations. Therefore, with technological advancements streamlining insurance processes significantly, companies can visualize a brighter future for customers and insurers.