The insurance industry is under increasing pressure to update the way they process insurance claims because of rising costs to process a claim, customer expectations for faster processing and increased competition from InsurTech competitors.
By collaborating with an experienced insurance software development company, insurance companies will be able to implement new AI-based systems that can automate the entire Insurance claims handling process, providing Insurance companies with the opportunity to receive a positive ROI by reducing costs by using more efficient processes to handle claims.
The use of AI in the automation of insurance claims is not a new technology, it is a strategic priority for the future of the insurance industry and represents the next step for insurance companies to modernize their traditionally manual, inefficient, and error-prone processes into a technology-based, scalable and data-driven business for sustainable business.
Why are Insurers Shifting toward AI-driven Automation?
Insurers are rapidly adopting AI technology, as the increasing workload and digitalization of customer demands make the lengthy and expensive process of handling claims manually unviable. The traditional process for handling claims involves handing off claims from department to department, storing claims-related data in silos, and making decisions based on subjective criteria.
These challenges lead to an increase in the length of time it takes to complete the claim process, inaccuracies in the processing of claims, and the increased cost of processing claims. Through the development of Touchless Claims Insurance, AI addresses these problems by enabling fully automated claims journeys from First Notice of Loss (FNOL) to Settlements for the simplest claims and supplementing Human Experts for the more complex claims.
According to industry leaders such as Aviva, AI is also changing the economics of insurance; for example, they report a 23-day reduction in the amount of time to perform liability assessments, which has transformed their business into one of speed and cost advantage.
Understanding AI in Claims Automation
AI is being leveraged to automate the claims process by using Machine Learning, Natural Language Processing, Computer Vision and Robotic Process Automation to take huge amounts of claims data to analyse and act upon. To do this, the AI systems will ingest a variety of unstructured inputs (e.g., images, PDFs, voice recordings), determine if the insured has coverage, detect fraud, estimate loss amounts, and route claim processing decisions in a very efficient manner.
The effectiveness of straight through processing (STP) will be very high for low risk claims, with STP rates moving from 10-20% to 70-80%, and the cost per claim will move from approximately $75 per claim to around $15 per claim.
The result is that when speed is improved through automation, customer satisfaction will improve, solver churn will decline, and more skilled resources will be released to perform higher value tasks.
Why Claims Automation Has Become a Business Priority?
Automating claims processing is essential for improving operational margins due to the fact that claims account for 60-70% of an organization’s operating expenses.
Increasing claim volumes
The increasing number of claims caused by natural disasters, surges in auto claim activity and the increase in the number of insureds is resulting in 15-20% year over year increases in the number of claims being processed every year.
Higher customer expectations
The current volume of claims being processed will continue to increase significantly in the future and the demand for e-commerce-type instant resolution of claims by digital natives has increased significantly in the last several years.
Regulatory demands for accuracy and documentation
The National Association of Insurance Commissioners (NAIC) and General Data Protection Regulation (GDPR) require transparency in claim decisions and for insurance claim processing to be completed without bias. The AI-enabled models used for processing claims can provide an explanation of the model and how the decision was made.
The rising cost of manual claims handling
Manual claims handling is a large expense for organizations. The average cost of handling a claim is approximately $75, while added fraud leakage can result in an additional cost of 5%-10%. AI processes can save 30%-50% compared to manual handling of claims, and the Boston Consulting Group (BCG) found that AI can potentially save organizations up to 50% in claims expenses.
The Business Case for AI in Claims Automation
AI dramatically improves the potential benefits realised through decreased expenses, higher levels of fidelity and faster processing times through quantifiable return-on-investment against the three pillars of Cost, Accuracy and Speed as follows :
Cost
Per-claim costs decreased from $75 to $15 as a result of implementing automation, in addition, total operational costs decreased by 30-50% with Scandinavian insurers providing examples of a 25% decrease in their expense ratios. Total annualised savings of over $6 million when the ratio of straight through processing rates is in excess of 20% and compounded further as the volume increases without increasing workforce levels.
Accuracy
AI removes 95% of manual data entry mistakes, and increases the ability of an organisation to identify fraudulent activity by 40% as a result of utilising machine learning (ML) techniques. ML models also achieve 90% or higher accuracy for triage and severity estimation, which reduces the amount of rework and leakage in the process.
Speed
The amount of time taken to process claims is reduced from weeks to hours (by 70%). Aviva has reduced the time to assess liability claims by 23 days, whereas the time to resolve simple claims is now real-time and consequently increased their NPS by 29%.
Key AI Use Cases in Claims Automation
Artificial Intelligence (AI) has changed how every stage of claims is processed in insurance and related industries through a specific set of applications.
Automating First Notice of Loss (FNOL):
By using voice, text, or image capture via mobile apps or chatbots, the FNOL process is instantly recorded; while natural language processing (NLP) captures all critical details, location validation through GPS indicates the true context of any incident. The result is: 80% of FNOLs now are in digital format, resulting in a reduction of call center volumes by 50%.
Document Processing of FNOLs:
The use of artificial intelligence (AI) for OCR capabilities allows accuracy of 99%, allowing for efficient data capture of handwritten medical forms, along with extraction of U.S. Medical Codes from those forms, and matching against policies through Natural Language Processing (NLP).
Detecting Insurance Fraud and Risk Prediction:
Machine Learning detects instances of abnormal behavior occurring across 100+ areas of measurement, including types of claims submitted in a certain geographic area; the detection of insurance fraud is now estimated to prevent losses of $6.5 Billion each year across the industry.
Automating the Estimation of Damage:
Computer Vision algorithms use images and/or video to determine the cost to repair damaged property, typically to within 5% of the true cost. The use of drone technology enables the reduction of on-site inspections by 70% and the ability for remote settlement of property damage claims.
Providing Customer Support through GenAI Chatbots:
Generative AI provides the capability to respond to 80% of customer status inquiries, document and provide escalation to agents. With the addition of multilingual capabilities, insurance companies are able to provide a global reach without the need for additional personnel.
Routing Claims and Automating Workflows:
Utilization of RPA and AI technologies simplify the process of routing claims and managing the Flow from First Notice to Final Resolution; the auto-settlement of Simple Claims occurs and the routing of more Complex Claims takes place through the Routing Models, achieving straight-through processing on 70% of claims.
How A3Logics Helps Insurers Transform Claim Operations?
A3Logics has created an all-in-one AI Claims Transformation service that incorporates established accelerators, such as their:
- Claims AI Platform: Pre-built ML for OCR, fraud, and triage that can be integrated into Guidewire/Duck Creek systems through APIs.
- Touchless Claims Implementation: 50% STP in 90 days with the ability to expand that to as much as 80%.
- Data Modernization: Extractions from legacy systems and data cleaning for AI training.
- ROI Guarantee: Cost reductions of 30-40% or free extension on the project after the cost savings have been realized.
- Case Study: A regional carrier was able to reduce its cycle times by 65% and save $4.2 million annually.
Conclusion
AI can improve the efficiency of claims processing by upwards of 70%, reduce costs by 30-50% and prevent 40% of claims fraudulent activity. Consequently, this technology is generating millions of dollars in new revenue opportunities for Claim Automation businesses and enhancing customer service. Touchless Claims Insurance at scale delivered by AI in Claim Automation transforms what were historically considered a cost center into how companies differentiate themselves from competitors, as the insurers that are slow to adopt AI risk losing market share due to their competitive disadvantages and will be unable to capture the benefits associated with improved economics and service. Strategic alliances with modernization specialists can help Claim Automation firms accelerate their return on Investment, guarantee compliance, scalability and future-proof their businesses in a market where insurance services are rapidly evolving with AI.
