We’ve previously discussed Artificial Intelligence (AI) and the effect it may have on industries going forward. If you missed it, you can catch up here. This week we’re going to look at how AI can specifically improve the claims lifecycle for insurers.
There are two main issues insurers have with implementing AI:
1. Legacy Systems
Traditionally, insurers used legacy systems, which were either built in-house or a one-time installation of a vendor solution. Unfortunately, software needs to be continually updated, and a system installed ten or even five years ago would be outdated, compared to the vast amount of technology solutions available today.
Legacy systems are difficult to integrate, and claims data often gets stored in silos. This makes data difficult to mine, and it’s almost impossible to generate informed decisions based on historical claims data. Over the past seven years, cloud-based claim systems have become more common. These systems are fully integrated to ensure all data is gathered in one centralized database, making it easier to implement analytical solutions.
2. Paper Files
Then there’s the case of claim data being stored in paper files. Paper files are inefficient as data can easily be lost, it’s harder to share in large claim departments, and claim data cannot be easily mined to identify patterns and trends. Paper files need to be replaced by a centralized electronic database.
What Part can AI play in the claims lifecycle?
As mentioned in part 1 of this blog, AI is excellent at processing structured and unstructured data to which it is presented. Imagine an insurer has 20 years of claim data available in their database; that’s a huge amount of information – which has so much potential. Consider the following scenarios:
- The Claim Duration can be predicted based on historical claim data. The more accurate the duration prediction, the better strategic planning can be put in place.
- The total cost of a specific claim can be predetermined, improving financial predictions, underwriting capability, and any cash flow issues.
- Claims can be assigned a risk rating. High-risk claims can be identified and passed on to a claims advisor for further analysis. Low-risk claims can be processed automatically, improving claim processing efficiency and reducing the time to payout for a claimant, depending on your company policy.
- Repeat Claims. The likelihood of a particular claimant to claim again can be predicted based on their personal information and claim history. This helps to identify potential high-risk claimants, as noted in point 3 above.
- Analyze doctor’s medical diagnosis to identify any suspicious patterns. Unfortunately, sometimes medical professionals help claimants to secure benefit payments. Reducing this fraudulent activity helps the insurer to reduce costs associated with fraud.
- Suggested next best action on a claim. AI can use past data to identify appropriate next steps for a claims advisor to take, improving claim processing efficiency.
These are just five hypothetical situations, but the possibilities are endless. Think robo-advisors offering quotes to customers, educated chatbots to answer questions on your website, and improved underwriting capabilities.
It will certainly be an interesting space to watch over the next few years!
Read more about these key considerations right here.