We are used to hearing, “Drive carefully” when leaving the house. While that is just an affectionate word of caution, what if there was a certainty attached to it? What if you could know beforehand that your vehicle would put you at peril? Today, injury prediction has made these big what ifs possible. Intelligence exists that can predict injuries. Data scientists have used the predictive ability of AI in countless fields. From predicting if footballers are likely to get injured on the field or not, to predicting if two people are compatible for dating.
At Claim Genius, we use injury prediction to determine if a vehicle that’s been in an accident is safe to drive again or not. It is one of the primary features of our Genius AI suite.
In this article you will find information on:
- The relevance of Injury Prediction.
- An Overview of Machine Learning and Datasets.
- The benefits of Claim Genius’ injury prediction services .
Why is injury prediction important?
Machines are less prone to calculation errors than us humans. No matter how thorough a manual inspection of an accident vehicle is, artificial intelligence will always be one step ahead. Our AI can provide deeper insights into crash statistics and the hidden seriousness of the accident.
To understand the process better, you need to have a clear understanding of two concepts related to artificial intelligence.
1. Machine Learning
Machine Learning (ML) is the ability of a trained system to learn and improve based on environment and experience, without needing to be explicitly programmed. An ML model is initially ‘trained’ using training data, which is basically a collection of data points which will start the ball rolling. After the ML model is trained using a large enough training set, it can start ‘thinking’ for itself. It will analyze a situation, map it to past experiences fed to it, and draw conclusions.
If it makes a mistake in drawing a conclusion, the ML model will record it so the error won’t be repeated again.
ML models are trained using primarily two methods:
This type of machine learning matches input to output labels attached to the training data. The AI can then determine whether the end result is correct. We call this technique supervised learning because the initial training period of the ML model requires a certain amount of handholding and label feeding.
This method uses unlabeled, unclassified data to teach the AI. The ML model has to deconstruct the input function and draw its own inferences. Using such inferences, the AI can keep evolving on newer conditions and scenarios. It will fall back on past experiences to understand how it should react to current input / stimulus.
For more in-depth resources on Machine Learning, read this insightful article.
A dataset is a collection of data points used as a training set for the ML model. The efficiency and accuracy of a machine learning algorithm relies on the quality of data fed to it during the training period. In our blog on data accuracy, we explain why your data needs to be as perfect as possible if you want maximum throughput from your AI.
A dataset should be as large as needed to allow the AI to learn from more real-life scenarios. At the same time, you need to make sure you are not feeding biased data to the algorithm. Otherwise, the ML model is bound to draw incorrect inferences. Data scientists have termed this problem as class imbalance. Class imbalances are going to be especially harmful if you’re going for a supervised learning approach. So, keep your data unbiased, balanced between positives and negative labels, and as error-free as possible. The AI will take care of the rest.
How Claim Genius uses data science for injury prediction
We have developed computer vision algorithms for assessing the state of a vehicle after it has been in an accident. We extensively use AI in damage detection which allows us to automate the vehicle inspection process. The plus point here is that we can also measure car damage against a gradation scale and not just point out whether there is damage or not. Our intelligence is able to scan a vehicle’s exterior through anyone can upload through the Claim Genius mobile AI app. This is all useful information that can be used as a training dataset for our AI.
Couple that with statistical analysis of overall accidents, and we can actually predict how safe you are in a vehicle that’s been in a collision or a scrape.
What does this mean for carriers and insurers?
Carriers have to make important decisions for their customers. They have to understand damage severity of the accident so they can relay that information back to the insurance agent / broker. Ultimately, the parties involved in the accident need to know the reimbursements needed. Sometimes, a vehicle is damaged beyond repair. If the carrier can arrive at this conclusion very fast, it means faster turnarounds on key decisions and finally, better client satisfaction.
What does this mean for end-customers?
Safety, above all else. If a carrier has performed injury prediction analysis, and has reported that the vehicle is likely to cause serious trouble, you’d feel better about going to the salvage yards. Even if the vehicle damage is not so severe as to warrant scrapping, you will at least be aware that your vehicle will stop working fine down the road. You can plan better, and take more informed decisions than you could’ve with a long-drawn manual inspection process.
Claim Genius believes in the power of automation. We use modern AI technology to enhance the process of vehicle damage detection and insurance decisions. Our AI performs injury prediction based on an analysis of accident impact severity and crash forces. Know more about the technology we use and see if we are a good fit for your insurance needs.