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Transforming Claims Processing with Artificial Intelligence and Machine Learning

Mar 7, 2024
AI & ML in Claims Processing

Every year, insurance carriers around the world face new problems, making it more difficult for them to sustain a profitable business while maintaining high customer satisfaction levels.

If you have dealt with traditional claims processing, you may be familiar with the bottlenecks that are associated with it. Such as:

  • The burden of inputting and confirming claims data manually
  • Remote locations visit for damage inspection
  • Long claims processing cycle impacts client engagement & retention
  • Overpayments due to inaccuracies in claim computations

The list is never-ending.

What is the Solution?

In the present era, insurance technology services like Machine Learning (ML), Robotic Process Automation (RPA), Artificial Intelligence (AI), etc., have opened up new avenues for decreasing turnaround times, improving real-time data collection, and predicting probable events that will result in improved client satisfaction levels.

Moreover, automation technologies such as artificial intelligence (AI), machine learning (ML), and P&C claims automation are transforming the insurance claim processing system for better business outcomes.

As per a recent report, global AI in the insurance market is expected to reach a value of USD 35.77 billion by 2030, at a CAGR of 33.06% during the forecast period.

global AI in the insurance market report

Insurance Claims Processing Revamped with AI & ML

Claims handling is one of the most important operations for insurance companies. Claims processing manages policyholder claims, from initial contact to case settlement responsibilities.

When used properly, AI and ML have much more to offer in terms of automating internal operations, such as an online claims portal, self-service FNOL intake and document processing, damage evaluation, and auto-adjudication.

Understanding with a Stat

According to Forbes, leveraging AI and intelligent automation to analyze and validate claims has improved claims accuracy by up to 99.99%, operational efficiency by 60%, and customer experience by 95%.

This blog will explore the areas where ML and AI can be utilized to accelerate overall claims processing for better client satisfaction.

Implementing AI and ML Solutions for Next-Gen Claims Processing – Digitizing the Customer Journey

To understand the influence of AI and ML on claims processing, we must first understand the role of big data.

Previously, storing huge volumes of data was a bottleneck due to storage limitations. However, data storage on current systems is much easier now. However, the identification of significant data within such vast datasets is still quite a challenging task.

Only artificial intelligence and its subfields, like machine learning, can extract usable information from massive amounts of data. In claims processing, such vast and usable data can be used to assess claims more precisely while detecting subtle differences that humans cannot detect.

Let’s now go through the top 4 ways in which automation improves claims processing.

Self-service FNOL intake:

eFNOLs are different from traditional ones. The claimant is not required to contact the insurer or deliver the documents in person.

Instead, users can enter information into a chatbot or mobile app driven by AI and ML, upload media assets and document scans, and assist insurers in processing claims more quickly and accurately.

Intelligent document processing:

IDP focuses on AI and optical character recognition (OCR) for the processing of physical documents.

This AI-powered technology can improve document quality, organize documents, and extract unstructured data from insurance documents that can be turned into meaningful structured data using natural language processing, deep learning, and computer vision.

Predictive analytics for claims triage:

Insurers experience the highest number of claims during peak seasons. They must prioritize claims promptly and assign them to the right person.

Leveraging AI-powered predictive analysis can help distinguish between claims that need immediate attention and can be automatically authorized and those that require human intervention.

Damage inspection using computer vision:

Traditionally, damage estimates for car claims were done manually in a repair shop or during an adjuster’s examination at the accident site, which is time-consuming. Machine learning can shorten this turnaround time. This system can compare uploaded smartphone photographs to a large database of damaged car images to evaluate the extent and projected cost of the damage.

Furthermore, inspecting damaged house roofs can be hazardous and time-consuming. As a result, insurance firms are spending more on computer vision and drone inspections with automated damage detection. Drone inspection services offer image detection tools that check for perfect roof wireframes and highlight problems in the image.

Approach to claim processing with AI

To Wrap Up

Several insurance carriers have already transitioned to partial automation for minor claims processing. Moving forward, more insurers are planning to embrace automation for even complex cases for enhanced policyholder satisfaction and reduced turnaround time. However, the human touch will always be indispensable.

From implementing automation in insurance claims processing and basic customer interaction using a chatbot to data interpretation leveraging OCR, KMG is here to refine your insurance operations in a better way. Our team provides specialized consultation for the seamless adoption of AI and ML in insurance business processes.

Reach out to us today!

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