By: Gareth Evans – Head of Customer Success UK, Ireland & The Nordics, Shift Technology 

The AI revolution is well underway in the insurance industry. From underwriting to claims management, traditional processes which rely on rules-based engines are gradually being replaced by dynamic tools powered by artificial intelligence (AI). This enables insurers to reduce costs, improve efficiency and enhance accuracy – and respond more quickly to events like the pandemic, telehealth revolution, or recent rise in extreme weather events. 

Though it’s by no means an exhaustive list, here are the top five ways insurers are dipping their toes into the AI ocean. 

• Improving fraud alert accuracy and condensing claims fraud investigations

When people think of insurance fraud, claims fraud – the fabrication or misrepresentation of the circumstances of a claim – is typically what comes to mind.

There are several ways AI can help insurers enhance how they handle fraud. The first hinges on making it easier to accurately identify fraud in the first place. Unlike rules-based engines, which can only be updated retrospectively (after a fraud trend has already been identified), AI can analyze huge quantities of claims data to spot anomalies in real time. This means less fraud slips through the net, and fewer innocent claims are flagged as potentially fraudulent. 

When a claim is flagged as potentially fraudulent, AI can also be implemented to automatically cross-check the myriad of auxiliary documents supporting the claim. This can condense fraud investigations into a matter of hours, rather than weeks. 

• Stamping out underwriting fraud 

Claims fraud is just one of many ways that consumers attempt to take advantage of insurance companies. Another is underwriting fraud, which can inject significant unknown risk into policies. 

Underwriting fraud can take many forms. The term encompasses all forms of fraud where policy applicants misrepresent themselves or their circumstances during the onboarding process to gain a pecuniary advantage. Unfortunately, with manual detection methods, wrongdoers are rarely caught. They often enjoy lower insurance premiums for years – resulting in significant lost revenue to insurers.

With dynamic AI-based applications, insurers can change this. Firstly, AI can quickly corroborate applicant data with third party information (e.g. from external third party databases) to identify fraudsters – even those who attempt to obscure their identities. 

Secondly, thanks to advanced data analytics, AI can quickly uncover fraud networks and identify agent gaming patterns during the underwriting process, rather than at the point a claim is filed. 

• Spotting recovery opportunities 

With a typical manual claims review process, and a broad range of handler experience it is easy for insurers to miss potential recovery opportunities – leading to unnecessary losses. 

By using AI to analyze every single claim for recovery opportunities, insurers can more accurately identify situations where a third-party is responsible for losses and initiate recovery action accordingly. 

AI can be trained to interpret both typed and handwritten claims a vast range of documents to identify recovery opportunities. What’s more, real-time analysis at scale means recovery opportunities can be identified without delaying claims resolution.

• Speeding up the entire claims lifecycle

Automating business processes is nothing new in the insurance industry. After all, insurers have been using rules-based engines or BPMs to automate part of the claims lifecycle for years. However, this piecemeal approach to automation has – in many cases – done little to tangibly accelerate the claims process, leaving many customers frustrated at insurers’ perceived sluggishness. 

A puzzle-piece approach to claims lifecycle automation often leaves errors, gaps in data, and accidental streams of misinformation. Instead, implementing an AI-based intelligent decision engine enables insurers to speed-up claims decisions at multiple touchpoints, including intake, fraud detection and subrogation.  

To illustrate the tangible difference, let’s examine a claim for a minor two-car accident that was flagged as potential fraud – both with and without intelligent decisioning in place.  

Without intelligent decisioning:A fraud flag comes in, triggered by the date of loss being close to the inception of the policy, prompting the claims team to input the case details into a system to detect any patterns of fraud. They have to manually review the supplied documentation, such as documents received, and information obtained at first notification of loss. Their findings are then compared with third party information obtained. Ultimately, the claim is correctly determined not to be fraudulent. But the customer has been left with a damaged car for weeks while the claim is processed.

With intelligent decisioning: While checking if all information has been submitted correctly, the system identifies that the date of loss is close to the inception of the policy. It alerts a the claims handler, giving them crucial context that the timeline is of potential concern. The following day the system receives documentation supplied relating to the recovery of the vehicle from the accident scene. It processes this document and records that the call out and collection dates and times are after the inception of the policy. Within days, the system has identified this information to the claim handler who confirms there’s been no fraud, enabling the policyholder’s claim to be resolved swiftly. 

• Enabling employees to achieve more with less time

As demonstrated, AI in insurance companies can take on a sizeable chunk of employees’ tedious administrative work. This is crucial at a time when increased levels of burnout, combined with the ‘great resignation’ and ‘great retirement’ are seriously impacting staffing levels in the insurance industry. Given this dwindling talent pool, it makes sense that insurers are looking to replicate the skills of their top performing employees by investing in AI tools that can ‘think’ like their best investigator, claims handler, or underwriter on their best days. 

However, it’s worth noting that AI in insurance isn’t a panacea. It works best when used alongside analysts’ in-depth expertise and people skills. Though AI can be instrumental in figuring out the ‘what’ (such as when fraud is being perpetrated), it often can’t understand the underlying ‘why’ without human help.

What’s next?

There’s a multitude of potential applications for AI in insurance companies – with new use cases being developed each day. 

In this fast-moving landscape, one thing’s certain. As forward-thinking insurers roll out wide-reaching intelligent automation initiatives, AI will quickly go from a ‘nice to have’ to a ‘must have’ for all insurance industry players. 

In the battle to remain competitive, long-term success will hinge notjust on implementing AI-based tools, but ensuring they work seamlessly alongside expert teams.