Putting AI to work

How AI Has Transformed The Way Insurance Works

Putting AI to work in Auto Insurance

The world is moving to a strange place, business-wise. Employees all over, spread across all possible sectors are working remotely. In these times when human contact and presence is looked down upon, we find we are relying more and more on machines and wireless tech to support our work structure. But in the auto-insurance industry, the complete cycle from damage detection to carrier underwriting can hardly be taken care of by machines that have to be told what to do. The overheads of supervision would far outweigh the benefits. It would be much better if humans did the job themselves. This is where AI comes into the picture. It can automate a lot of complicated processes of the insurance industry. What’s more, AI can work independently, without constant human assistance. Combined with certain allied fields (enumerated next), AI can act on its own, without needing human assistance or even presence.

The sub-branches of AI, which make automation possible, are:

Machine learning

This is the ‘feeding’ part of the automation process. Machine learning is a concept in which you expose the still-raw AI to training data sets and train it to predict outcomes. As we already discussed on our blog on data accuracy, the quality of the data sets you use at this stage are going to determine the quality of your AI throughput.

Deep learning

Plain predictions can only get you so far. To replace human intelligence, you’d want your AI to analyse the data it sees and draw pertinent conclusions from it. Deep learning happens when you ‘teach’ your system to understand the data it is presented, as a human might. It can come handy during the carrier underwriting process, where you wouldn’t want to leave room for doubt about the accuracy of your underwriting decisions.

Computer Vision

Computer Vision is a branch of AI that mimics the ability of the human eye. Through CV, AI can detect images, break them down, and make sense of them. CV can be used in image recognition and pattern recognition. For CV to work, your AI needs to be fed quality training data, after which it can work autonomously.

Neural Networks

Neural networks are structures that imitate the functioning of the human brain. These are complex entities that work on the basis of a feedback mechanism and a bias to add weight to the inputs (not unlike the human way of weighing one’s decisions). A neural network accepts inputs, processes them, and produces output, similar to the biological thought. You can add as many layers to a neural network as you want and make it as complex as your problem requires.

The journey to complete automation is a long one, and you may need to train and test your AI in stages before you can say for sure that your intelligence can run the business in a contactless world. This blog will walk you through the AI technology that supports automation in the following 3 fields:

1. AI Automation in Insurance Claims

In the insurance domain, insurtechs are working to integrate AI such that the tedious tasks become faster, cheaper and more accurate. Knowing just where to insert the artificial intelligence makes all the difference. You wouldn’t want to replace human intelligence in the tasks that require the human touch. Auto-insurance has become so popular because it takes care of all the right avenues of the business. This includes bypassing a physical inspection of the vehicle, assessing the severity of the damage, and estimating the amounts that the faulty party is liable to pay.

A lot of insurance settlements still rely heavily on manual, paper-based processes which are cumbersome and filled with bureaucracies. Besides, manual intervention means less room for flexibility, meaning that sometimes, customers end up paying more than needed just because the underwriting process could not adapt to the unique circumstance. The turnaround time is brutally slow, and often, nobody emerges a winner even after the claim is settled.

But times are changing…

Insurtechs are on the verge of bringing about radical change. There are mobile apps which can report back with a complete analysis, based on the photos of the damaged vehicle. You simply have to click the photos and upload them via the app, and everything else is taken care of. Interested in being a changemaker, Claim Genius also has its own mobile app, the APIs of which can easily integrate with the carrier’s own app. We facilitate ease and comfort in the insurance claims settlement process.

Besides, AI can also provide a behavioural pricing model. Here, safer drivers (identified by data on the past driving incidents and patterns) are liable to pay less insurance as a result of their clean records. AI is projected to keep growing in the auto-claims sector, seeing its power in tailor-made and swift results. Add to that the ability of virtual claim settlement and you have a sure shot projection of the rise and relevance of AI automation.

 

2. Artificial Intelligence in Damage Detection

We have discussed AI’s role in damage detection extensively in a previous blog. Simply put, AI uses a technique called as computer vision, to imitate the working of the human eye. Like a trained eye, AI automates vehicle inspection, and also damage severity. With an impressive speed, it can generate a comprehensive report which you can take back to your customers in no time.

Computer vision depends on supervised learning, in which you basically handhold your expert system throughout the learning process. Slowly and steadily, with quality data sets, your intelligent system learns to separate out a complete image into components. You can teach your AI how a healthy part looks like and it will perform a comparative analysis to identify whether a part is really damaged. As mentioned before, you can provide a bias to teach your AI parts grading and severity analysis. Feed your expert system enough test cases, and rest assured that it will better itself and its detection analysis without your help.

 

3. Intelligent Fraud Detection

According to Fraud Benchmark Report by cybersource 83% of North American businesses conduct manual reviews, and on an average, they review 29% of orders manually.

One cannot take all insurance claims at face values. Fraudulent claim cases are all too common. There are people, having spent many a year in the industry, who have gained a keen insight into claims and can make out a fraud case based on instinct alone. Yes, this humane sentiment has not yet seeped into artificial intelligence, but as a carrier, you have to consider the cost-benefit ratio. Manual inspection is time-consuming and costly. It takes away from your ability to get back to your customers in a timely fashion.

AI automation can help. If you have a repository of past cases, it might be time to blow off the dust settled on them. By digitizing past cases and feeding it to your intelligence, you can create reference points and benchmarks for your expert system. By sifting through countless patterns, your AI will learn to interpret tendencies.

Organizations using AI for fraud detection typically work in three phases: train, test, validate. AI engineers then build a model using a variety of labels / classifications, with several outputs associated with inputs. Each model converges into a decision tree, which in turn gives a binary/boolean result: yes or no.

With AI based fraud detection, you get three major benefits: speed, scale and efficiency.

Claim Genius’s Contribution

Claim Genius has not been a mere spectator of the changes in the auto-claims industry. We have been active participants in the AI revolution. We have offerings for claim settlement as well as damage detection. Also, we are providing a FREE demo of our tools! Anybody who first wants to dip their toes in before taking a dive is welcome. All you have to do is fill out the form on our Contact page and put in a request. We will get back to you in no time.

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