Claims operations are the heart of property and casualty insurance, it’s where insurers spend the most time and resources, every single day, as well as a property and casualty insurer’s biggest cost component. According to Deloitte research, paid losses combined with investigation and settlement costs represented more than two thirds of premiums collected in the US in 2020. It also happens to be the area of insurance operations where arguably the largest opportunities for cost savings lie.
The trajectory of automation in insurance is steep. It’s rise is driven simultaneously by cost reduction pressure, the new normal of customer expectations around digital claims and omnichannel service, as well as the nature of the insurance market which is highly sensitive to consumer preferences due to the prevalence of carrier switching.
Claims processing today has hit an awkward stage between manual processing and optimal integration of technology. Traditionally, claims processing was the bastion of insurance adjusters. Adjusters would manually review all claims, looking for incomplete information or fraud indicators.
Between then and now, claims processing has benefited from the addition of technology, especially automation tools. Most recently the COVID-19 digital transformation push has accelerated adoption of automation in insurance claims processing from single digits in 2018 to as high as 55%. In fact, McKinsey predicts that in 2030, the majority of claims activities will be automated, though claims processing will remain at the core of insurance operations.
To keep up, claims processing fitness now requires speed, accuracy, and unmatched customer experiences. And underwriting, fraud detection, and risk analysis all need to be faster and more accurate, too.
That’s why claims leaders have been chipping away at the entire claims management process chain, diligently applying new ways to automate or augment with technology for faster, more efficient processing. And proliferating automation solutions across the claims processing lifecycle—like chatbots for triage or RPA to connect systems—are making a difference against the goals of reducing costs and increasing efficiency, but they may also be driving customers away and many projects have hit a wall in terms of scale.
At the same time as internal and customer pressures continue to rise, consolidation in the insurance industry has hit new highs in many markets around the world, with M&A activity reported up 10% in the US alone, just seven months into 2021.
Despite the twists and turns, automation is still tapped to be kingmaker, with Salesforce research reporting that growth-oriented insurers see automation as essential to attaining top performance.
Instead of chasing marginal improvements with existing automation tools, overcome the shortfalls of standard automation technologies for claims processing with AI.
Where is this gap? Unstructured data.
The disconnect between workflows, where manual intervention that’s not adding customer experience value is taking place, is in extracting claims report data.
For example, the new customer demand for digital claims processing means insurers are struggling to process the claims that now arrive in diverse formats, including emails, texts, chat, and images.
There’s a lot of unstructured claims report data, and it is only increasing in volume.
Before unstructured claims report data can be fed into any automated workflow it must be transformed, by hand, to extract and format all of the data received in the myriad of possible shapes, sizes, and formats for images, videos, messages, letters and forms.
AI-driven claims report data extraction, offers a step change in insurance claim processing accuracy and speed, strengthening risk management, fraud detection, and customer satisfaction.
Super.AI’s solutions, such as Super.Image, break down unstructured data into components. The combined intelligence of trained human labellers, which could include your current claims adjusters for example, and AI are then used to ramp up automated processing with unmatched accuracy, in less than a day, using minimal historical data for training.
Super.AI enables you to extract, classify, and analyze unstructured data from insurance claims, incident reports, loss notifications, and loss estimates at scale faster and more accurately than ever before. Extracted claims information can be used by fraud detection models and to support risk analysis and underwriting.
Automated insurance claims processing improves accuracy by eliminating data extraction errors and accelerates claims handling thanks to rapid data extraction. The growing pool of timely, accurate, data also serves to enhance underwriting, for better and faster risk analysis and fraud detection. All of which contribute to improved service delivery, in turn supporting increased customer satisfaction.