The motor insurance industry is one of the biggest industries in the automotive market with a valuation of about $800 Billion. However, the claims process is still very slow, tedious, and a costly affair. 

Insurance companies can significantly cut down on the time and cost associated with this process by leveraging AI for the right processes. 

In this blog, we will discuss one such process i.e. repair estimations, the current challenges in this process, how AI can help solve these challenges, and what are some limitations that you will have to prepare for while using these AI models. 

Let’s dive in. 

What is a repair estimate? And why is it essential for motor insurers? 

When purchasing or leasing a car, it is mandatory to buy insurance with it. Driving an uninsured car is not legally permissible in a lot of countries. 

This insurance comes handy when a car requires bigger repairs. Since auto repairs are usually costly, owning an insurance allows a car owner to be able to afford these repairs without burning a hole in their pockets. 

When a customer raises a claim for repairs, the insurance will generate an estimate for the cost of repairs. This estimate relies on two different parties – 

• A body shop’s repair appraiser who generates the repair cost estimates
• An insurance company’s appraiser who determines how much money you can claim for these repairs. 

However, the traditional framework of repair estimations is very complex and involves a lot of subjectivity at every step. Let’s talk about this in a little more detail. 

Unravelling the complexity of repair estimates

What make repair estimates so complex? There are quite a few reasons why this happens –

• Repair estimates are inconsistent – When a car owner raises a claim based on a body shop’s estimate, the insurer either tries negotiating with the body shop to lower the price, or might suggest a different body shop that can do it at a cheaper price.
  
  The traditional approach of repair and claim estimations relies on human judgement, which is often subjective and hence, very inconsistent. This inconsistency and discrepancy in claim estimates is a big pain point causing conflict between car owners and the insurer.

• Repair estimates rely on the actual cash value – The actual cash value (ACV) of a car refers to the current replacement cost of the vehicle minus depreciation over time. Repair estimates are often calculated based on the vehicle’s ACV, which can often be quite lower than the vehicle’s original purchase price.
  
  In a lot of cases, a customer might be unhappy with the repair estimates provided by the insurance company, because they believe the car is actually worth more than its ACV. In these cases, they might raise a dispute asking for more money than the estimates generated.

• Repair estimates are subject to inter-company arbitration – Insurance claims that are subject to a dispute go through an inter-company arbitration. This usually occurs when two insurance companies, representing different parties involved in a motor vehicle accident, want to decide who is responsible and has to pay for the damages, often leaving the car owners in a confused state without any clarity for a long time. 

All of the cases mentioned above make the repair estimation process unnecessarily long, tedious, and require multiple follow-ups at every step, making it very cumbersome for the final customer, and leaving them with an overall terrible experience. 

These issues, however, can be easily fixed by using the right AI models to get the job done, reducing time, money, and effort at every step of the process. 

How can AI help solve these challenges

Using the right artificial intelligence models can not only save time and effort of the repair estimation process, they can also remove uncertainty at a lot of steps and increase the overall efficiency of the process. 

AI can be implemented in the repair estimation process to – 

1. Automate detecting damage and report generation
   
   Damage detection and reporting is crucial for generating repair estimation and processing claims. The current vehicle inspection process requires human intervention to detect and trac every detail, which is tiring, and is prone to errors due to subjectivity. 

However, when using an AI model that is trained to detect damage, you can very easily increase the speed of this process, and also increase accuracy and remove subjectivity, which will in turn reduce errors in the process, and help you provide more accurate repair estimates.

• Fetch OEM and Market Data
  
  Unlike humans, AI can very easily work with large datasets and process a lot of information quickly.
  
  Using AI that can fetch OEM and market data will allow insurers to very easily estimate the cost of the damage, connect the car owner with the right repair network, and also fetch the right price for repairs and replacements, which will reduce the risk of subjectivity during repair estimates and cut down on the hassle of prolonged negotiations in the process.

• Enable STP (Straight Through Processing) for motor claims

Straight Through Processing, or STP, is a method used to simplify and speed up the auto claim process.

STP uses AI to assess a claim and make recommendations for repairs. A car owner can simply submit pictures of the vehicle damage and get repair estimates on their device within a few minutes.

This process helps reduce errors, improve consistency, and drives faster settlement for claims. Along with this, it also frees up claim handlers to focus on more complex tasks, and gives the car owner a delightful experience in the process. 

Overall, using an AI model for your repair estimation and claims processing makes a lot of sense since it makes the process less cumbersome, speeds things up, and also removes the risk of subjectivity, errors, and fraud in the process. 

A lot of companies, for example, Inspektlabs (a popular AI-powered vehicle inspection tool) have collaborated with Estimatics providers and repair networks to give you a proper end-to-end solution to make the whole process easier. 

Limitations of AI-based estimations

Artificial intelligence is still in its development phase. While it is pretty efficient in automating a lot of processes, it also comes with its own set of challenges that cannot be ignored. For instance – 

• AI works great for external damage inspection and analysis. It might not be ideal for analysing internal damage and will require human intervention for accurate reports.
• AI often does not have context of new vs. old damage and this might lead to exaggerated damage reports during the claims process. 
• Using an AI model that is still very new and not trained on enough data will result in false reporting, causing more harm than good. 
• AI can also be tricked to report false damages by switching vehicles mid process, causing the model to confuse them as one.

While these limitations exist, a lot of companies are building systems within their AI models to overcome these challenges. In fact, quite a few of them are already pretty efficiently mitigating these limitations and giving insurance companies a fool-proof end-to-end solution to ease their claims process. 

Conclusion 

The repair estimation process during claim settlements have traditionally been manual, unnecessarily long, and quite cumbersome. 

However, this entire process can very easily be automated with the right AI tools, saving time, money, and effort, allowing humans to focus on more critical tasks, and also give car owners a better experience. 

While using these AI models comes with its own set of challenges, there are a lot of companies solving these challenges and providing great end-to-end solutions to ease the entire claims process. So if you’re looking for a way to boost your process, make sure to check them out!