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Claims processing is one of the most important and involved aspects of insurance, and as such, it means that many insurers could probably make significant improvements to their business model by streamlining the claims process.
With the advent of truly useful artificial intelligence and machine learning, a lot of industries both inside and outside the tech world are beginning to realise just how significant these advances are. While the insurance sector has been slow to catch on, we're now seeing more and more signs that AI is the next big thing in insurance.
Let's take a look at how artificial intelligence can aid insurers and, specifically, improve claims processing for everyone involved.
What is claims processing?
Claims processing is the entire process of managing policyholder claims; it covers all stages of claims from initial contact to the closing of the case, including triaging claims, review, fraud investigation, adjustment if necessary, and finally either the acceptance or rejection of the claim itself.
Claims processing, of course, applies to claims from across all areas of insurance. Similar pathways might be taken when assessing claims for low-value items as when assessing claims for more complicated situations, such as in the case of negligence or professional indemnity claims, and yet these two types of claims are at opposite ends of the scale when it comes to the level of judgement and involvement required.
While much of the work involved in claims processing is complex and nuanced, equally large parts of claims processing consist of time-consuming administrative tasks, which most insurers would probably rather outsource. Claims processing is fraught with legal and technical checks which must be made before a claim can be accepted, and these checks are not particularly difficult to perform.
It's in this arena that artificial intelligence and machine learning can help.
What does AI have to do with claims processing?
Artificial intelligence might not quite be at the level that sci-films of the 1980s predicted, but today's AI is capable of so much more than it was even just a decade ago, in large part due to the rise of accessible, affordable computing power.
Today, AI can be used in almost any field to streamline working practices and improve efficiency, which in turn can often mean passing more value onto customers. The insurance industry is no different, with modern insurtech software allowing insurers to use AI to automate or partially automate particular tasks.
Machine learning is the next step up; advanced AI which is capable of machine learning can improve over time, learning new patterns and recognising trends in data. If you want to know more about the role of machine learning in insurance, you can read our machine learning guide.
Machine learning, with the help of modern computers, can be applied to big data sets - the kind that humans just can't process themselves - to analyse vast sets of data and identify risk more accurately than ever before.
The applications of AI in the claims process
To understand how AI can be fully utilised in claims processing, it's first important to understand more about this relationship between AI and big data. We've written before about how important big data is going to be in insurance here.
Big data refers to huge volumes of information which previously wouldn't have been possible to collect due to limitations in data storage. However, technology today allows modern computers to hold terabytes of data without so much as breaking a sweat.
But how can we find meaning in datasets this big? Only with AI. Artificially intelligent software is the only way to make sense of big data, which is why insurers are turning to AI to revolutionise the way they work.
Big data has applications across insurance, affecting risk assessment and underwriting as well as claims processing. In claims processing, this abundance of data (and finally, the ability to do something useful with it) means that claims can be assessed more accurately than ever before, picking up nuance that human brains can't see.
From the moment that a customer opens a claim, AI can streamline the claims process for both customer and staff. Automating administrative tasks with AI software means that claims are processed much more quickly, and employee workload lightens.
Claims investigations become more accurate over time with the help of machine learning: being more able to spot 'red flags' for false or illegitimate claims means genuine claims can be processed, accepted and resolved much more quickly.
All of this makes for a smoother customer experience, better service, and happier staff. The savings made by streamlining the claims process can also be passed onto customers in the form of lower premiums, or further added value - whatever works.
The future of claims processing
Using artificial intelligence to automate administrative claims processing is really just the tip of the iceberg. In fact, in very basic terms, many small and uncomplicated claims have been semi-automated for years - for example, high-volume, low-cost claims including windshield claims have been mostly automated for decades.
But what AI allows insurers to do is expand their capability for automation to even more complicated claims, where decision-making is key. Machine learning allows AI software to study behavioural analytics and customer data to make more accurate decisions on whether a claim is genuine, and this can be applied to ever more nuanced claim types.
While AI isn't yet ready to take on those high-value negligence and business disruption claims we talked about earlier all on its own, the foundations of AI technology are there to enable even the most complicated claims to be at least partially automated over the coming years.
The biggest hurdle for claims managers at the moment will likely be in ensuring that AI technologies and practices can be smoothly integrated into current ways of working. For insurers still very much at the beginning of this transition, simple AI software which bundles multiple operations and technologies into one can be an effective way to take steps forward without being overwhelmed by new advancements.