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Juggling Act – Half 1: Remodeling the claims house


In this series of blogs, my colleagues and I will look at the insurance sector in emerging markets with a particular focus on technology, digitization, platforms and ecosystems.

Basically, that’s what insurers do to pay for claims. in fact, it accounts for the lion’s share of their spending. For P&C insurers, for example, this is typically 60 to 80 percent of the cost.

The simplicity of this premise, of course, hides a great deal of complexity, and insurers have to balance three elements, which are often contradicting:

  • Included payment losses – pay what is appropriate, and just what is appropriate
  • Get customer satisfaction – Customers generally do not have much contact with their insurers, except in the claims situation, which is the “moment of truth”.
  • Keep the cost off Claims management

While it might be easy enough to balance two of these, each combination often comes at the expense of the third. So you can keep customers happy and administrative costs close to zero by paying in full for each claim. However, your loss ratio, the most important KPI, goes through the roof. Alternatively, you can manually edit each claim and know with absolute certainty that you are only paying for what you should: your payment loss KPI is great, but you have high costs and unhappy customers.

Finding this balance has been the challenge for the industry from the start. Two out of three were the best she could do – until now. The technology is redesigning the disbursement room for claims.

Pay first

At Accenture, we have long sought to help customers industrialize the claims management process so that it works like a factory and only takes as much time on claims as it takes to pay the right costs. This means automating the claims process and only branching out when necessary. The constant goal is to optimize the balance between the time spent and the impact on the payout result.

AI and analytics have revolutionized the possible. As I wrote before IIn China, the size of the market has forced insurers to take a digital route. As a result, they are global leaders in the use of data, artificial intelligence (AI) and analytics, optimizing the full spectrum of insurance processes, from underwriting to claim payment. Today, some Chinese insurers have reversed the claims process: they pay by default to balance all three aspects. This is how they do it.

The key is to create a direct payment process by default, for which technology is increasingly providing better solutions. However, this requires a cultural shift in which insurers change their minds about finding no reason not to pay and instead of quickly paying any claim except for those where there is a reasonable reason to delay payment or discontinue.

Step one is to implement more complex workflow solutions so that instead of all of the above claims, say $ 10,000, only those claims that deviate from a pre-defined approach, that is, those that trigger data analysis flags, are checked. Indeed, our studies show that the “leakage” (the cost of managing or paying claims) is proportionally higher for small, large-volume claims that are “uninteresting” than for the large ones that are normally audited.

The second option is to use analytics to compare the data of each claim with that of its peers and look for outliers. Why does this windshield replacement cost three times the average? Why is this insured person making a third claim between the same two people within one year? There may be good reasons, but maybe not. This is better than fixed rule systems as they are too general. (For example, one customer had 80 percent of claims marked with a red flag, and as a result, operators clicked off any flag because they didn’t have time to check whether they were valid.)

ThThe third is using AI at key decision points – like ping an with damage detection, if the insured sends photos of his car after an accident and the system estimates the likely costs. This approach is also helpful for more complex areas such as hospital claims. Critical Illness Insurer Xiang Hu Bao, has fully automated its claims settlement system, for example Use of AI and blockchain to enable the filing of digital evidence.

AI and predictive analytics can be used at other decision points to enable an automated standard path payment or stop the process. These points include coverage compliance, liability assessment, fraud detection and final payment decision.

Lessons for everyone

Insurers elsewhere can learn from the approach taken by Chinese insurers to increase claims accuracy, reduce leakage and increase customer satisfaction.

Data is critical. Many insurers try to integrate multiple systems, paper reports and information in external databases. Without the technological tools with which data is retrieved from various sources and stored in structured databases, this is almost impossible on a scale – for example with the help of AI and optical character recognition (OCR) to extract data from written documents and feed it into structured databases, or to analyze legal documents or police reports of accidents.

Once that data is in, however, insurers can use AI and analytics to drive automation, make payouts the standard, and ensure claims adjusters are spending their time more valuable and doing more interesting things.

Insurers can also use technology to increase the second element – customer satisfaction. I’ll explore that in my next blog.

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