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Juggling – Part 1: Transforming the Damage Plant




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, paying claims is what insurers do; in fact, it is the lion's share of their spending. For non-life insurers, for example, it usually amounts to 60-80 percent of the costs.

The simplicity of this premise naturally masks a lot of complexity and insurers have to balance three elements that are often at odds with each other:

  • Includes payment losses – pay what is appropriate and only what is appropriate
  • Maintaining customer satisfaction – customers generally do not have much contact with their insurance companies except in the claim situation, which is the "moment of truth"
  • Keep down the cost of claims administration

Although It may 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 each claim in full. however, your loss ratio, the most important CPI, will go through the roof. Alternatively, you can manually process each claim and determine with absolute certainty that you are only paying the ones you should: your payment losses KPI will be fantastic, but you will have high costs and unhappy customers.

Getting this balance right has been the industry's challenge since its inception, with two of the three best they could do – until now. Technology is reworking the payout space.

First, pay up

At Accenture, we have long sought to help customers industrialize the claims management process, making it work like a factory with just as much time on claims as is necessary to pay what is right. This means automating the damage processes and then branching only if necessary. The constant goal is to optimize the balance between time spent and the impact on the payout result.

AI and analysis have revolutionized what is possible. As I wrote earlier, in n China, the scale of the market insurers were forced to follow a digital path. As a result, they have become global leaders in the use of data, artificial intelligence (AI) and analytics – streamlining the full range of insurance processes, from guarantee to payment of claims . Today, some Chinese insurers have inverted the claims process: their standard is to pay, so that they can balance all three aspects. This is how they do it.

The key is to build a direct payment process as standard, for which technology has gradually provided better solutions. However, this requires a cultural change, where insurers change their attitude from finding a reason not to pay, and instead of paying each claim quickly except those where there is a good reason to delay or stop payment.

Step one is to implement more sophisticated workflow solutions so that instead of escalating all the above statements, for example USD 10,000 to a supervisor, they only check the statements that deviate from a predefined approach, ie. In fact, our studies show that "leakage" (the cost of administering or paying out claims) is proportionally higher with small, large volumes that are "uninteresting" than with the large ones that are usually examined.

Second is to use analytics to compare the data of each claim against its peers and look for deviations. Why does this windshield replacement cost three times the average? Why does this insured do a third damage between the same two people in one year? There may be good reasons, but there may not be. This is better than systems with fixed rules, because they are too generic. (For example, a client saw that 80 percent of the claims were red-flagged, with operators consequently clicking off each flag because they did not have time to see if they were valid.)

The third is to use AI in important decision points – as Ping An does with damage recognition where the insured sends photographs of his car after an accident, and the system estimates the probable cost. This approach is also useful for more complex areas such as hospital requirements. Critical Illness Insurers Xiang Hu Bao, for example, has fully automated its judicial review system which uses AI and blockchain to enable the submission of digital evidence.

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

Lessons for all

Insurers elsewhere can learn from the method developed by Chinese insurers to increase damage accuracy, reduce leakage and increase customer satisfaction. [19659003] Data is crucial. Many insurance companies try to integrate several systems, paper reports and information contained in external databases. It is almost impossible to scale without the technical tools that retrieve data from various sources and place it in structured databases – for example using AI and optical character recognition (OCR) to extract data from written documents and enter them into structured databases or to analyze legal documents or police reports on accidents.

Once the data is in place, insurance companies can apply AI and analytics to drive automation, making payments standard and ensuring that claimants spend their time more valuable and have more interesting work.

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

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