Claims processing is a mission-critical activity for life, health, home, and auto insurance providers. Speed, accuracy, and efficiency in claims processing has a direct impact on an insurer’s ability to foster customer satisfaction and retention, prevent claims leakage, and drive business profitability.
The most digitally advanced insurance providers are already leveraging AI-driven software tools to transform their operations. In this blog, we will highlight five different ways for insurance providers to streamline or automate insurance claims processing with help from AI technology. We will also share some of the potential benefits and top reasons why your insurance company should start supporting its claims processing activities with AI.
Why You Should Automate Insurance Claims Processing with AI
1. Accelerate Claims Resolution
When an insurance plan member or policyholder submits a claim, they’re counting on a speedy resolution process that finalizes their settlement as quickly as possible. Data shows that speed of claims resolution drives customer satisfaction rates more than any other factor that insurance providers can control. Regardless of the outcome, taking longer to settle a claim results in lower satisfaction rates for the policyholder.
AI-driven software tools can allow insurance providers to accelerate their claims processing activities, deliver rapid claims resolution even during periods of high volume, and ensure high rates of customer satisfaction and retention.
2. Eliminate Errors and Inconsistencies
Insurance companies know that errors and inconsistencies in claims processing have a negative impact on business outcomes. Mistakenly denying or under-paying a claim results in a poor customer experience, while improper overpayment of claims (also known as claims leakage) cuts into profitability and drives higher premiums.
Claims leakage is estimated to cost U.S. insurance companies between $30 billion and $67 billion annually, much of which is the result of errors and inconsistencies in claims processing. Even U.S. Medicaid reported $50.3 billion in improper payments for the 2023 fiscal year.
Insurance companies can leverage AI-driven software tools to reduce or eliminate errors and inconsistencies in claims processing that drive claims leave and poor customer experiences.
3. Fight Back Against Fraud
Fraudulent activity is another major source of claims leakage for insurance companies operating in the United States and around the world. Common examples include provider fraud and benefits fraud in health insurance, false theft claims in home insurance, and staged accidents in auto insurance.
According to the Coalition Against Insurance Fraud (CAIF), health insurance providers are losing as much as $300 billion annually due to healthcare fraud. The CAIF also reported that 10% of property-casualty claims involve fraudulent activity.
Data-driven insurance companies can use AI-driven software tools to detect and prevent fraud. Equipped with machine learning (ML) technology, insurance companies are now capable of analyzing large sets of insurance claims data and training sophisticated algorithms to detect suspicious activity at a scale that would be impossible for human analysts to match.
4. Optimize Customer Experience and Retention
In a consumer survey, 15% of respondents identified a lack of digital capabilities as the most significant challenge while interacting with insurers. Additionally, 41% of respondents said they were likely or more likely to switch insurance providers due to a lack of digital capabilities.
Modern consumers prefer insurance providers that invest in digital capabilities that help optimize the customer experience. This includes using AI-driven software to automate and accelerate claims processing, offering a web-based claims portal to provide transparency throughout the insurance claims process, and even AI-powered conversational chatbots that help customers navigate the claims process.
Most customers still see a need for human interaction during the claims journey, but the most successful insurance businesses are lowering their costs and strengthening customer retention by supporting the customer experience with AI.
5 Ways to Automate Insurance Claims Processing with AI Technology
1. Automating Claims Submissions with AI Image Processing
Insurance companies can deploy AI image processing technology to extract and digitize information from images and video at scale. This can include digital images of claim forms and First Notice of Loss (FNOL) documents, photographs of property or vehicle damage, or even video of an incident that triggered an insurance claim.
Insurance companies can leverage multiple kinds of AI technology to process images. These include:
- Optical Character Recognition (OCR) – OCR technology converts an image of text into a machine-readable text format. An OCR engine can digitize claims forms much faster than a human data entry clerk, resulting in a faster time-to-resolution for the claim.
- Computer Vision – Computer vision is a field of AI that focuses on training computers to accurately interpret photographic images and video. Computer vision can be used in insurance to assess:
- compliance with work safety procedures (using surveillance video from an insured job site),
- wildfire risk or the condition of insured property (by analyzing aerial imagery), or
- vehicle damage (by analyzing images from an accident report).
2. Automating Claims Assessment and Loss Estimation with AI-Driven Analysis
Large insurers employ teams of claims adjudicators who work around the clock to assess a continuous stream of new insurance claims. Assessing claims is a meticulous and inherently error-prone process that involves reviewing policy documents and cross-referencing them with insurance claims to determine how much compensation each claimant should receive.
Claims assessment is an expensive and time-consuming activity for insurance providers. It’s also a great candidate for automation with help from AI.
Modern AI-driven data analytics software can analyze documents and images from an insurance claim, then cross-reference the data with policy documents or other predefined rules. When AI image processing and analytics capabilities are combined, you get computer programs that can ingest data about a vehicle (e.g. make, model, production year, etc.), assess damage to the vehicle by processing an image of the accident scene, then cross-reference the results with vehicle value data to estimate the losses.
3. Anticipating Insurance Trends with Predictive Analytics
Predictive analytics is an area of machine learning where historical data is used to train algorithms that can predict future outcomes. Large insurers have access to huge amounts of historical data that can be used to support predictive analytics applications.
Predictive analytics tools can help insurance companies anticipate insurance trends by leveraging historical data to predict the answers to questions like:
- What will be our total cost of claims this month/quarter/year?
- What is our exposure to liability in case of a catastrophic event?
- Which medications or treatments are becoming more popular?
- Which types of claims are increasing or decreasing in frequency?
- Which customers are most likely to switch providers?
The ability to anticipate trends with predictive analytics can help insurance companies allocate their resources more efficiently to seize business opportunities and respond to potential challenges.
4. Identifying Fraud and Claims Leakage with ML Algorithms
Insurance fraud is on the rise and it’s a significant challenge for insurers to filter out the fraudulent claims from the millions of legitimate ones they process and pay each year.
Today, the process of identifying fraudulent claims relies heavily on the skills of human analysts who score claims based on their value and the presence of fraud indicators to determine which claims warrant further investigation. This process is time-consuming at scale for human analysts, but can be streamlined with help from machine learning algorithms.
Insurance companies can leverage historical claims data (from both legitimate and fraudulent claims) to train ML algorithms that can detect indicators of fraud with a high degree of accuracy and make decisions about when it’s worthwhile to investigate a potentially fraudulent claim.
5. Automating Customer Interactions with an AI Chatbot
Insurance companies can deploy AI chatbots to fulfill a variety of business use cases, including things like:
- Triaging Insurance Customers – An AI chatbot installed on an insurance provider’s website can help streamline site navigation or triage customers to services they want to access.
- Supporting Claims Submission – An AI chatbot can guide claimants through the claims submission process, from accepting a claim report to providing initial information and supporting documentation or evidence.
- Delivering Customer Assistance – An AI chatbot can provide interactive customer assistance, enabling customers to access details of their coverage by asking questions in a conversational format and without having to comb through lengthy policy documents.
AI chatbots won’t replace human agents, but they can certainly make it cheaper and more convenient for customers to interact with insurers, access services, and accomplish their goals.
Partnering with Hakkōda to Implement AI Technology in Your Claims Processing Workflows
Insurance companies in 2024 are upgrading digital systems to take advantage of AI technology at every level of their operations. The most successful of these companies will establish a competitive advantage in the form of reduced costs, greater efficiency, and happier customers.
Hakkoda brings deep industry experience in the financial services and insurance space together with expertise across the modern data stack to help our clients get the most out of their data technology investments, whether they’re just beginning their migration to Snowflake’s Financial Services Data Cloud or looking to use the latest GenAI tools to transform their processes and shorten time-to-insight at scale.
Ready to start leveraging AI to transform your claims processes? Let’s talk today.
